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├── Getting Started with GPU Development.sh
└── README.md
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<h1 align="center">
<img src="https://user-images.githubusercontent.com/45159366/138614105-18a013cb-dad0-4e79-b9ff-2430207622e7.png">
<br />
GPU Guide
</h1>
#### A guide covering how a GPU works including the applications, libraries, hardware, and tools. It will also give you a better understanding of how GPU-based tasks work in embedded systems, mobile phones, personal computers, professional workstations, and game consoles.
**Note: You can easily convert this markdown file to a PDF in [VSCode](https://code.visualstudio.com/) using this handy extension [Markdown PDF](https://marketplace.visualstudio.com/items?itemName=yzane.markdown-pdf).**
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/138614110-fc44b809-0239-423d-8e24-e0b85549613b.png">
<br />
</p>
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/138614121-28110c11-cc0b-4115-8335-8cb30a6bfe32.png">
<br />
How the CPU and GPU work together when running application code.
</p>
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/138614114-a0fdd83a-b885-42b8-849f-f45691091454.png">
<br />
Unified Memory Architecture
</p>
# Table of Contents
1. [GPU Learning Resources](https://github.com/mikeroyal/GPU-Guide#gpu-learning-resources)
2. [Electric charge, field, and potential](https://github.com/mikeroyal/GPU-Guide#electric-charge-field-and-potential)
- Charge and electric force (Coulomb's law): Electric charge, field, and potential
- Electric field: Electric charge, field, and potential
- Electric potential energy, electric potential, and voltage: Electric charge, field, and potential
3. [Circuits](https://github.com/mikeroyal/GPU-Guide#Circuits)
- Ohm's law and circuits with resistors: Circuits
- Circuits with capacitors: Circuits
4. [Magnetic forces, magnetic fields, and Faraday's law](https://github.com/mikeroyal/GPU-Guide#magnetic-forces-magnetic-fields-and-Faradays-law)
- Magnets and Magnetic Force: Magnetic forces, magnetic fields, and Faraday's law
- Magnetic field created by a current: Magnetic forces, magnetic fields, and Faraday's law
- Electric motors: Magnetic forces, magnetic fields, and Faraday's law
- Magnetic flux and Faraday's law
5. [Electromagnetic waves and interference](https://github.com/mikeroyal/GPU-Guide#electromagnetic-waves-and-interference)
- Introduction to electromagnetic waves: Electromagnetic waves and interference
- Interference of electromagnetic waves
6. [Geometric optics](https://github.com/mikeroyal/GPU-Guide#Geometric-optics)
- Reflection and refraction: Geometric optics
- Mirrors: Geometric optics
- Lenses
7. [Linear Algebra](https://github.com/mikeroyal/GPU-Guide#Linear-Algebra)
8. [Virtualization](https://github.com/mikeroyal/GPU-Guide#virtualization)
9. [Parallel Computing](https://github.com/mikeroyal/GPU-Guide#Parallel-Computing)
10. [OpenCL Development](https://github.com/mikeroyal/GPU-Guide#opencl-development)
11. [CUDA Development](https://github.com/mikeroyal/GPU-Guide#cuda-development)
12. [Algorithms](https://github.com/mikeroyal/GPU-Guide#algorithms)
13. [Machine Learning](https://github.com/mikeroyal/GPU-Guide#machine-learning)
14. [Deep Learning Development](https://github.com/mikeroyal/GPU-Guide#Deep-Learning-Development)
15. [Computer Vision Development](https://github.com/mikeroyal/GPU-Guide#computer-vision-development)
16. [Gaming](https://github.com/mikeroyal/GPU-Guide#gaming)
17. [Game Development](https://github.com/mikeroyal/GPU-Guide#game-development)
18. [OpenGL Development](https://github.com/mikeroyal/GPU-Guide#opengl-development)
19. [Vulkan Development](https://github.com/mikeroyal/GPU-Guide#vulkan-development)
20. [DirectX Development](https://github.com/mikeroyal/GPU-Guide#directx-development)
21. [Professional Audio/Video Development](https://github.com/mikeroyal/GPU-Guide#professional-audiovideo-development)
22. [3D Graphics & Design](https://github.com/mikeroyal/GPU-Guide#3d-graphics-and-design)
23. [Apple Silicon](https://github.com/mikeroyal/GPU-Guide#Apple-Silicon)
24. [Core ML Development](https://github.com/mikeroyal/GPU-Guide#core-ml-development)
25. [Metal Development](https://github.com/mikeroyal/GPU-Guide#Metal-development)
26. [MATLAB Development](https://github.com/mikeroyal/GPU-Guide#matlab-development)
27. [C/C++ Development](https://github.com/mikeroyal/GPU-Guide#cc-development)
28. [Python Development](https://github.com/mikeroyal/GPU-Guide#python-development)
29. [R Development](https://github.com/mikeroyal/GPU-Guide#r-development)
30. [Julia Development](https://github.com/mikeroyal/GPU-Guide#julia-development)
# GPU Learning Resources
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
[Graphics Processing Unit (GPU)](https://en.wikipedia.org/wiki/Graphics_processing_unit) is a circuit that's composed of hundreds of cores that can handle thousands of threads simultaneously. GPUS can rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. They are used in embedded systems, mobile phones, personal computers, professional workstations, and game consoles.
[Random Access Memory (RAM)](https://en.wikipedia.org/wiki/Random-access_memory) is a form of computer memory that can be read and changed in any order, typically used to store working data and machine code. A random access memory device allows data items to be read or written in almost the same amount of time irrespective of the physical location of data inside the memory, in contrast with other direct-access data storage media.
[Video Random Access Memory (VRAM)](https://en.wikipedia.org/wiki/VRAM) is the RAM allocated to store image or graphics related data. It functions in the same way as RAM, storing specific data for easier access and performance. Image data is first read by the processor and written on the VRAM. It is then converted by a [RAMDAC](https://en.wikipedia.org/wiki/RAMDAC) or a RAM digital-to-analog converter and display as graphics output.
[Graphics Double Data Rate (GDDR) SDRAM](https://en.wikipedia.org/wiki/GDDR6_SDRAM#GDDR6X) is a type of synchronous graphics random-access memory (SGRAM) with a high bandwidth ("double data rate") interface designed for use in graphics cards, game consoles, and high-performance computing.
[Integrated Graphics Processing Unit (IGPU)](https://en.wikipedia.org/wiki/Graphics_processing_unit#Integrated_graphics_processing_unit) is a component built on the same die (integrated circuit) with the CPU ([AMD Ryzen APU](https://www.amd.com/en/processors/ryzen-with-graphics) or [Intel HD Graphics](https://en.wikipedia.org/wiki/Intel_Graphics_Technology)) that utilizes a portion of the computer's system RAM rather than dedicated graphics memory.
[Tensor](https://en.wikipedia.org/wiki/Tensor) is an algebraic object that describes a multilinear relationship between sets of algebraic objects related to a vector space.Objects that tensors may map between vectors, scalars, and other tensors.
[Tensors](https://www.tensorflow.org/guide/tensor) are multi-dimensional arrays with a uniform type (called a dtype).
[Tensor Cores](https://www.nvidia.com/en-us/data-center/tensor-cores/) are an AI inference accelerator in NVIDIA GPUs that provide an order-of-magnitude higher performance with reduced precisions like TF32, bfloat16, FP16, INT8, INT4, and FP64, to accelerate scientific computing with the highest accuracy needed.
[RT (Real-time ray tracing) Cores](https://developer.nvidia.com/blog/nvidia-turing-architecture-in-depth/) is a hardware-based ray tracing acceleration accelerate Bounding Volume Hierarchy (BVH) traversal and ray/triangle intersection testing (ray casting) functions. RT Cores perform visibility testing on behalf of threads running in the SM, allowing it to handle another vertex, pixel, and compute shading work.
[Central Processing Unit (CPU)](https://en.wikipedia.org/wiki/Central_processing_unit) is a circuit that's composed of multiple cores that executes instructions comprising a computer program. The CPU performs basic arithmetic, logic, controlling, and input/output (I/O) operations specified by the instructions in the program. This is different from other external components such as main memory, I/O circuitry, and graphics processing units (GPUs).
[AMD Accelerated Processing Unit (APU)](https://en.wikipedia.org/wiki/AMD_Accelerated_Processing_Unit) a series of 64-bit microprocessors from Advanced Micro Devices (AMD), designed to act as a central processing unit (CPU) and graphics processing unit (GPU) on a single die.
[Vector Processor](https://en.wikipedia.org/wiki/Vector_processor) is a central processing unit (CPU) that implements an instruction set where its instructions are designed to operate efficiently and effectively on large one-dimensional arrays of data called vectors.
[Digital Signal Processing (DSP)](https://en.wikipedia.org/wiki/Digital_signal_processing) is the application of a digital computer to modify an analog or digital signal. It's wadely used in many applications including video/audio/data communications and networking, medical imaging and computer vision, speech synthesis and coding, digital audio and video, and control of complex systems and industrial processes.
[Image Signal Processing (ISP)](https://en.wikipedia.org/wiki/Image_processor) is the processs of converting an image into digital form by performing operations like noise reduction, auto exposure, autofocus, auto white balance, HDR correction, and image sharpening with a Specialized type of media processor.
[Application Specific Integrated Circuits (ASICs)](https://en.wikipedia.org/wiki/Application-specific_integrated_circuit) is an integrated circuit (IC) chip customized for a particular use in embedded systems, mobile phones, personal computers, professional workstations, rather than intended for general use.
[Single Instruction, Multiple Data (SIMD)](https://en.wikipedia.org/wiki/SIMD) is a type of parallel processing that describes computers with multiple processing elements that perform the same operation on multiple data points simultaneously.
[What Is a GPU? Graphics Processing Units Defined | Intel](https://www.intel.com/content/www/us/en/products/docs/processors/what-is-a-gpu.html)
[Deep Learning Institute and Training Solutions | NVIDIA](https://www.nvidia.com/en-us/training/)
[Deep Learning Online Courses | NVIDIA](https://www.nvidia.com/en-us/training/online/)
[Existing University Courses | NVIDIA Developer](https://developer.nvidia.com/educators/existing-courses)
[Using GPUs to Scale and Speed-up Deep Learning | edX](https://www.edx.org/course/using-gpus-to-scale-and-speed-up-deep-learning)
[Top GPU Courses Online | Coursera](https://www.coursera.org/courses?query=gpu&page=1)
[CUDA GPU Programming Beginner To Advanced | Udemy](https://www.udemy.com/course/cuda-gpu-programming-beginner-to-advanced/)
[GPU computing in Vulkan | Udemy ](https://www.udemy.com/course/vulkan-gpu-computing/)
[GPU Architectures Course | Unversity of Washington](https://courses.cs.washington.edu/courses/cse471/13sp/lectures/GPUsStudents.pdf)
# Electric charge, field, and potential
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
- Charge and electric force (Coulomb's law): Electric charge, field, and potential
- Electric field: Electric charge, field, and potential
- Electric potential energy, electric potential, and voltage: Electric charge, field, and potential
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/127784122-2c0e9166-ff73-44cf-a5b5-62d76aba80c7.png">
<br />
</p>
**Electric Potential Energy. Source: [sparkfun](https://learn.sparkfun.com/tutorials/what-is-electricity/electric-potential-energy)**
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/127784121-8f89a787-1674-4db4-ba46-b2f280706dd9.png">
<br />
</p>
# Circuits
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
- Ohm's law and circuits with resistors: Circuits
- Circuits with capacitors: Circuits
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/127784127-ad587152-0c0d-4671-b29d-9231889899ff.png">
<br />
</p>
**Electric Circuits. Source: [sdsu-physics](http://sdsu-physics.org/physics180/physics180B/Chapters/electric_currents.htm)**
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/127784128-941e1cf9-0cff-4702-b97b-f827d9489a20.png">
<br />
</p>
**Symbols of Circuits .Source: [andrewpover.co.uk](https://andrewpover.co.uk/category/physics/)**
# Magnetic forces, magnetic fields, and Faraday's law
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
- Magnets and Magnetic Force: Magnetic forces, magnetic fields, and Faraday's law
- Magnetic field created by a current: Magnetic forces, magnetic fields, and Faraday's law
- Electric motors: Magnetic forces, magnetic fields, and Faraday's law
- Magnetic flux and Faraday's law
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/127784132-d4b67030-9e04-41f1-90d2-98b7c6beebe8.png">
<br />
</p>
**Magnetic Field. Source: [vecteezy](https://www.vecteezy.com/vector-art/593998-physics-science-about-the-movement-of-magnetic-fields-positive-and-negative)**
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/127784663-28e8ad0e-aa9d-4dbf-b285-0b2976de2d3e.png">
<br />
</p>
**Amphere's Law. Source: [sdsu-physics](https://sdsu-physics.org/physics180/physics196/Topics/magneticFields30.html)**
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/127784666-dd53913b-33c9-4ddd-8cf1-b8fa398bf3de.png">
<br />
</p>
**Farady's law. Source: [sdsu-physics](http://sdsu-physics.org/physics180/physics196/Topics/faradaysLaw.html)**
# Electromagnetic waves and interference
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
- Introduction to electromagnetic waves: Electromagnetic waves and interference
- Interference of electromagnetic waves
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/127785050-a98f0812-d723-49b9-9796-ac05bb64804a.png">
<br />
</p>
**Electromagnetic Wave. Source: [differencebetween](https://www.differencebetween.com/difference-between-wave-and-particle-nature-of-light/)**
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/127785051-1a86c310-0e09-4f8b-be8e-99770dd0301e.png">
<br />
</p>
**EMI Spectrum. Source: [electrical4u](https://www.electrical4u.com/electromagnetic-interference/)**
# Geometric optics
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
- Reflection and refraction: Geometric optics
- Mirrors: Geometric optics
- Lenses
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/127784685-dfeb8063-7bf2-4420-9610-41c05a0b5d4d.png">
<br />
</p>
**Geometric Optics - Raytracing. Source: [sdsu-physics](https://sdsu-physics.org/physics180/physics180B/Topics/light/raytracing.html)**
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/127784688-5ecef6ad-3520-41fd-a206-e675ca3a1f5b.png">
<br />
</p>
**Geometric Optics - Reflection. Source: [sdsu-physics](https://sdsu-physics.org/physics180/physics180B/Topics/light/raytracing.html)**
# Linear Algebra
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124998626-9a1b4680-e001-11eb-9a49-1e97604e8a10.png">
<br />
</p>
# Linear Algebra Learning Resources
[Linear algebra](https://en.wikipedia.org/wiki/Linear_algebra) is the math of vectors and matrices. The only prerequisite for this guide is a basic understanding of high school math concepts like numbers, variables, equations, and the fundamental arithmetic operations on real numbers: addition (denoted +), subtraction (denoted −), multiplication (denoted implicitly), and division (fractions). Also, you should also be familiar with functions that take real numbers as inputs and give real numbers as outputs, f : R → R.
[Linear Algebra - Online Courses | Harvard University](https://online-learning.harvard.edu/course/linear-algebra)
[Linear Algebra | MIT Open Learning Library](https://openlearninglibrary.mit.edu/courses/course-v1:OCW+18.06SC+2T2019/about)
[Linear Algebra - Khan Academy](https://www.khanacademy.org/math/linear-algebra)
[Top Linear Algebra Courses on Coursera](https://www.coursera.org/courses?query=linear%20algebra)
[Mathematics for Machine Learning: Linear Algebra on Coursera](https://www.coursera.org/learn/linear-algebra-machine-learning)
[Top Linear Algebra Courses on Udemy](https://www.udemy.com/topic/linear-algebra/)
[Learn Linear Algebra with Online Courses and Classes on edX](https://www.edx.org/learn/linear-algebra)
[The Math of Data Science: Linear Algebra Course on edX](https://www.edx.org/course/math-of-data-science-linear-algebra)
[Linear Algebra in Twenty Five Lectures | UC Davis](https://www.math.ucdavis.edu/~linear/linear.pdf)
[Linear Algebra | UC San Diego Extension](https://extension.ucsd.edu/courses-and-programs/linear-algebra-1)
[Linear Algebra for Machine Learning | UC San Diego Extension](https://extension.ucsd.edu/courses-and-programs/linear-algebra-for-machine-learning)
[Introduction to Linear Algebra, Interactive Online Video | Wolfram](http://www.wolfram.com/wolfram-u/introduction-to-linear-algebra/)
[Linear Algebra Resources | Dartmouth](https://math.dartmouth.edu/~trs/linear-algebra-resources.php)
# Defintions
### i. Vector operations
We now define the math operations for vectors. The operations we can perform on vectors ~u = (u1, u2, u3) and ~v = (v1, v2, v3) are: addition, subtraction, scaling, norm (length), dot product, and cross product:
The dot product and the cross product of two vectors can also be described in terms of the angle θ between the two vectors.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124398163-26a7cb00-dcc9-11eb-9b70-3452d50762c5.png">
<br />
</p>
**Vector Operations. Source: [slideserve](https://www.slideserve.com/krystal/streaming-simd-extension-sse)**
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124399554-fbc17500-dcd0-11eb-96b0-c31df664425a.png">
<br />
</p>
**Vector Operations. Source: [pinterest](https://www.pinterest.com/pin/41799102767414798/)**
### ii. Matrix operations
We denote by A the matrix as a whole and refer to its entries as aij .The mathematical operations defined for matrices are the following:
• determinant (denoted det(A) or |A|)
Note that the matrix product is not a commutative operation.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124398167-2ad3e880-dcc9-11eb-9455-9b0c3c6171cd.png">
<br />
</p>
**Matrix Operations. Source: [SDSU Physics](https://sdsu-physics.org/math/pages/435_LA_ch2.html)**
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124398169-2c051580-dcc9-11eb-934a-71c691325062.png">
<br />
</p>
**Check for modules that allow Matrix Operations. Source: [DPS Concepts](https://dspconcepts.com/forums/audio-weaver-designer/347-check-modules-allow-matrix-operations)**
### iii. Matrix-vector product
The matrix-vector product is an important special case of the matrix product.
There are two fundamentally different yet equivalent ways to interpret the matrix-vector product. In the column picture, (C), the multiplication of the
matrix A by the vector ~x produces a linear combination of the columns of the matrix: ~y = A~x = x1A[:,1] + x2A[:,2], where A[:,1] and A[:,2] are the first and second columns of the matrix A. In the row picture, (R), multiplication of the matrix A by the vector ~x produces a column vector with coefficients equal to the dot products of rows of the matrix with the vector ~x.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124398176-36bfaa80-dcc9-11eb-82c9-641f9ddd8563.png">
<br />
</p>
**Matrix-vector product. Source: [wikimedia](https://commons.wikimedia.org/wiki/File:Matrix_vector_product_qtl1.svg)**
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124398178-38896e00-dcc9-11eb-94c4-2b2fa7499989.png">
<br />
</p>
**Matrix-vector Product. Source: [mathisfun](https://www.mathsisfun.com/algebra/scalar-vector-matrix.html)**
### iv. Linear transformations
The matrix-vector product is used to define the notion of a linear transformation, which is one of the key notions in the study of linear algebra. Multiplication by a matrix A ∈ R m×n can be thought of as computing a linear transformation TA that takes n-vectors as inputs and produces m-vectors as outputs:
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124398182-3fb07c00-dcc9-11eb-815a-d31e591fe218.png">
<br />
</p>
**Linear Transformations. Source: [slideserve](https://www.slideserve.com/hall-cobb/chap-6-linear-transformations)**
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124398184-40e1a900-dcc9-11eb-90e9-36031124610f.png">
<br />
</p>
**Elementary matrices for linear transformations in R^2. Source:[Quora](https://www.quora.com/What-is-a-linear-transformation)**
### v. Fundamental vector spaces
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124398197-4fc85b80-dcc9-11eb-9abc-3741bda9b63e.png">
<br />
</p>
**Fundamental theorem of linear algebra for Vector Spaces. Source: [wikimedia](https://en.wikipedia.org/wiki/Fundamental_theorem_of_linear_algebra)**
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124398199-50f98880-dcc9-11eb-8fd0-994dc48f62d6.png">
<br />
</p>
**Fundamental theorem of linear algebra. Source: [wolfram](https://mathworld.wolfram.com/FundamentalTheoremofLinearAlgebra.html)**
# Computational Linear Algebra
### i. Solving systems of equations
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124398355-307dfe00-dcca-11eb-8668-ba037de5aca5.png">
<br />
</p>
**System of Linear Equations by Graphing. Source: [slideshare](https://www.slideshare.net/JITENDRATHAKOR/systems-of-linear-equations-43550732)**
### ii. Systems of equations as matrix equations
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124398360-34118500-dcca-11eb-94eb-81ee66a9dc8a.png">
<br />
</p>
**Systems of equations as matrix equations. Source: [mathisfun](https://www.mathsisfun.com/algebra/systems-linear-equations-matrices.html?ref=binfind.com%2Fweb)**
# Computing the Inverse of a Matrix
In this section we’ll look at several different approaches for computing the inverse of a matrix. The matrix inverse is unique so no matter which
method we use to find the inverse, we’ll always obtain the same answer.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124398409-71761280-dcca-11eb-9add-d105af886569.png">
<br />
</p>
**Inverse of 2x2 Matrix. Source: [pinterest](https://www.pinterest.com/pin/375276581446966518/)**
### i. Using row operations
One approach for computing the inverse is to use the Gauss–Jordan elimination procedure.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124398532-0c6eec80-dccb-11eb-9c82-6a65916fa43a.png">
<br />
</p>
**Elementray row operations. Source: [YouTube](http://www.youtube.com/watch?v=DH2JSYx52nk)**
### ii. Using elementary matrices
Every row operation we perform on a matrix is equivalent to a leftmultiplication by an elementary matrix.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124398426-83f04c00-dcca-11eb-8dde-54deedff30f7.png">
<br />
</p>
**Elementary Matrices. Source: [SDSU Physics](http://sdsu-physics.org/math/pages/435_LA_ch2.html)**
### iii. Transpose of a Matrix
Finding the inverse of a matrix is to use the Transpose method.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124398574-34f6e680-dccb-11eb-8112-ca77aad96004.png">
<br />
</p>
**Transpose of a Matrix. Source: [slideserve](https://www.slideserve.com/jaimin/matrix-inverse-and-transpose)**
# Other Linear Topics
In this section discuss a number of other important topics of linear algebra.
### i. Basis
Intuitively, a basis is any set of vectors that can be used as a coordinate system for a vector space. You are certainly familiar with the standard basis for the xy-plane that is made up of two orthogonal axes: the x-axis and the y-axis.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124398635-a040b880-dccb-11eb-8d5f-0e6ec65b742d.png">
<br />
</p>
**Basis. Source: [wikimedia](https://en.wikipedia.org/wiki/Basis_(linear_algebra))**
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124398636-a171e580-dccb-11eb-8a40-8e6a9e24b6af.png">
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</p>
**Change of Basis. Source: [wikimedia](https://en.wikipedia.org/wiki/Change_of_basis)**
### ii. Matrix representations of linear transformations
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<img src="https://user-images.githubusercontent.com/45159366/124398653-b9e20000-dccb-11eb-8985-1d203f52f131.png">
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**Matrix representations of linear transformations. Source: [slideserve](https://www.slideserve.com/sylvie/two-dimensional-geometric-transformations)**
### iii. Dimension and Basis for Vector Spaces
The dimension of a vector space is defined as the number of vectors in a basis for that vector space. Consider the following vector space S = span{(1, 0, 0),(0, 1, 0),(1, 1, 0)}. Seeing that the space is described by three vectors, we might think that S is 3-dimensional. This is not the case, however, since the three vectors are not linearly independent so they don’t form a basis for S. Two vectors are sufficient to describe any vector in S; we can write S = span{(1, 0, 0),(0, 1, 0)}, and we see these two vectors are linearly independent so they form a basis and dim(S) = 2. There is a general procedure for finding a basis for a vector space. Suppose you are given a description of a vector space in terms of m vectors V = span{~v1, ~v2, . . . , ~vm} and you are asked to find a basis for V and the dimension of V. To find a basis for V, you must find a set of linearly independent vectors that span V. We can use the Gauss–Jordan elimination procedure to accomplish this task. Write the vectors ~vi as the rows of a matrix M. The vector space V corresponds to the row space of the matrix M. Next, use row operations to find the reduced row echelon form (RREF) of the matrix M. Since row operations do not change the row space of the matrix, the row space of reduced row echelon form of the matrix M is the same as the row space of the original set of vectors. The nonzero rows in the RREF of the matrix form a basis for vector space V and the numbers of nonzero rows is the dimension of V.
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<img src="https://user-images.githubusercontent.com/45159366/124398664-c5352b80-dccb-11eb-866e-61808bcf953d.png">
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</p>
**Basis and Dimension. Source: [sliderserve](https://www.slideserve.com/kalil/chapter-3-vector-space)**
### iv. Row space, columns space, and rank of a matrix
Recall the fundamental vector spaces for matrices that we defined in Section II-E: the column space C(A), the null space N (A), and the row space R(A). A standard linear algebra exam question is to give you a certain matrix A and ask you to find the dimension and a basis for each of its fundamental spaces. In the previous section we described a procedure based on Gauss–Jordan elimination which can be used “distill” a set of linearly independent vectors which form a basis for the row space R(A). We will now illustrate this procedure with an example, and also show how to use the RREF of the matrix A to find bases for C(A) and N (A).
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124398677-d67e3800-dccb-11eb-8052-dfd1b01920fd.png">
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**Row space and Column space. Source: [slideshare](https://www.slideshare.net/VishveshJasani/row-space-column-space-null-space-rank-nullity)**
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<img src="https://user-images.githubusercontent.com/45159366/124398679-d8e09200-dccb-11eb-8707-e340933063d4.png">
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**Row space and Column space. Source: [slideshare](http://www.slideshare.net/RonakMachhi/null-space-rank-and-nullity-theorem)**
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<img src="https://user-images.githubusercontent.com/45159366/124398681-d9792880-dccb-11eb-8c6f-0c483cf860a4.png">
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**Rank and Nullity. Source: [slideshare](https://www.slideshare.net/VishveshJasani/row-space-column-space-null-space-rank-nullity)**
### v. Invertible matrix theorem
There is an important distinction between matrices that are invertible and those that are not as formalized by the following theorem. Theorem. For an n×n matrix A, the following statements are equivalent:
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<img src="https://user-images.githubusercontent.com/45159366/124398692-e6961780-dccb-11eb-98d9-dae7199a6365.png">
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**Invertible Matrix theorem. Source: [SDSU Physics](https://sdsu-physics.org/math/pages/435_LA_ch2.html)**
### vi. Determinants
The determinant of a matrix, denoted det(A) or |A|, is a special way to combine the entries of a matrix that serves to check if a matrix is invertible or not.
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<img src="https://user-images.githubusercontent.com/45159366/124398703-f9105100-dccb-11eb-8d4e-e6db343304ec.png">
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**Determinant of a Square Matrix. Source: [stackexchange](https://math.stackexchange.com/questions/1354148/proving-the-formula-for-finding-the-determinant-of-a-square-matrix)**
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<img src="https://user-images.githubusercontent.com/45159366/124398705-fa417e00-dccb-11eb-8d1f-0cbf01d77f3d.png">
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**Determinant of matrix. Source: [onlinemathlearning](https://www.onlinemathlearning.com/matrix-determinants.html)**
### vii. Eigenvalues and eigenvectors
The set of eigenvectors of a matrix is a special set of input vectors for which the action of the matrix is described as a simple scaling. When a matrix is multiplied by one of its eigenvectors the output is the same eigenvector multiplied by a constant A~eλ = λ~eλ. The constant λ is called an eigenvalue of A.
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<img src="https://user-images.githubusercontent.com/45159366/124398717-03cae600-dccc-11eb-86f2-72a816b9231a.png">
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**Generalized EigenVectors. Source: [YouTube](https://www.youtube.com/watch?v=xyhaYHGZN-w)**
### viii. Linear Regression
[Linear regression](https://en.wikipedia.org/wiki/Linear_regression) is an approach to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable.
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<img src="https://user-images.githubusercontent.com/45159366/124398722-0af1f400-dccc-11eb-9ac7-1de7c6e43ff5.png">
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**Multiple Linear Regression. Source: [Medium](https://medium.com/@subarna.lamsal1/multiple-linear-regression-sklearn-and-statsmodels-798750747755)**
# Virtualization
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
[HVM (Hardware Virtual Machine)](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/virtualization_types.html) is a virtualization type that provides the ability to run an operating system directly on top of a virtual machine without any modification, as if it were run on the bare-metal hardware.
[PV(ParaVirtualization)](https://wiki.xenproject.org/wiki/Paravirtualization_(PV)) is an efficient and lightweight virtualization technique introduced by the Xen Project team, later adopted by other virtualization solutions. PV does not require virtualization extensions from the host CPU and thus enables virtualization on hardware architectures that do not support Hardware-assisted virtualization.
[Network functions virtualization (NFV)](https://www.vmware.com/topics/glossary/content/network-functions-virtualization-nfv) is the replacement of network appliance hardware with virtual machines. The virtual machines use a hypervisor to run networking software and processes such as routing and load balancing. NFV allows for the separation of communication services from dedicated hardware, such as routers and firewalls. This separation means network operations can provide new services dynamically and without installing new hardware. Deploying network components with network functions virtualization only takes hours compared to months like with traditional networking solutions.
[Software Defined Networking (SDN)](https://www.vmware.com/topics/glossary/content/software-defined-networking) is an approach to networking that uses software-based controllers or application programming interfaces (APIs) to communicate with underlying hardware infrastructure and direct traffic on a network. This model differs from that of traditional networks, which use dedicated hardware devices (routers and switches) to control network traffic.
[Virtualized Infrastructure Manager (VIM)](https://www.cisco.com/c/en/us/td/docs/net_mgmt/network_function_virtualization_Infrastructure/3_2_2/install_guide/Cisco_VIM_Install_Guide_3_2_2/Cisco_VIM_Install_Guide_3_2_2_chapter_00.html) is a service delivery and reduce costs with high performance lifecycle management Manage the full lifecycle of the software and hardware comprising your NFV infrastructure (NFVI), and maintaining a live inventory and allocation plan of both physical and virtual resources.
[Management and Orchestration(MANO)](https://www.etsi.org/technologies/open-source-mano) is an ETSI-hosted initiative to develop an Open Source NFV Management and Orchestration (MANO) software stack aligned with ETSI NFV. Two of the key components of the ETSI NFV architectural framework are the NFV Orchestrator and VNF Manager, known as NFV MANO.
[Magma](https://www.magmacore.org/) is an open source software platform that gives network operators an open, flexible and extendable mobile core network solution. Their mission is to connect the world to a faster network by enabling service providers to build cost-effective and extensible carrier-grade networks. Magma is 3GPP generation (2G, 3G, 4G or upcoming 5G networks) and access network agnostic (cellular or WiFi). It can flexibly support a radio access network with minimal development and deployment effort.
[OpenRAN](https://open-ran.org/) is an intelligent Radio Access Network(RAN) integrated on general purpose platforms with open interface between software defined functions. Open RANecosystem enables enormous flexibility and interoperability with a complete openess to multi-vendor deployments.
[Open vSwitch(OVS)](https://www.openvswitch.org/)is an open source production quality, multilayer virtual switch licensed under the open source Apache 2.0 license. It is designed to enable massive network automation through programmatic extension, while still supporting standard management interfaces and protocols (NetFlow, sFlow, IPFIX, RSPAN, CLI, LACP, 802.1ag).
[Edge](https://www.ibm.com/cloud/what-is-edge-computing) is a distributed computing framework that brings enterprise applications closer to data sources such as IoT devices or local edge servers. This proximity to data at its source can deliver strong business benefits, including faster insights, improved response times and better bandwidth availability.
[Multi-access edge computing (MEC)](https://www.etsi.org/technologies/multi-access-edge-computing) is an Industry Specification Group (ISG) within ETSI to create a standardized, open environment which will allow the efficient and seamless integration of applications from vendors, service providers, and third-parties across multi-vendor Multi-access Edge Computing platforms.
[Virtualized network functions(VNFs)](https://www.juniper.net/documentation/en_US/cso4.1/topics/concept/nsd-vnf-overview.html) is a software application used in a Network Functions Virtualization (NFV) implementation that has well defined interfaces, and provides one or more component networking functions in a defined way. For example, a security VNF provides Network Address Translation (NAT) and firewall component functions.
[Cloud-Native Network Functions(CNF)](https://www.cncf.io/announcements/2020/11/18/cloud-native-network-functions-conformance-launched-by-cncf/) is a network function designed and implemented to run inside containers. CNFs inherit all the cloud native architectural and operational principles including Kubernetes(K8s) lifecycle management, agility, resilience, and observability.
[Physical Network Function(PNF)](https://www.mpirical.com/glossary/pnf-physical-network-function) is a physical network node which has not undergone virtualization. Both PNFs and VNFs (Virtualized Network Functions) can be used to form an overall Network Service.
[Network functions virtualization infrastructure(NFVI)](https://docs.vmware.com/en/VMware-vCloud-NFV/2.0/vmware-vcloud-nfv-reference-architecture-20/GUID-FBEA6C6B-54D8-4A37-87B1-D825F9E0DBC7.html) is the foundation of the overall NFV architecture. It provides the physical compute, storage, and networking hardware that hosts the VNFs. Each NFVI block can be thought of as an NFVI node and many nodes can be deployed and controlled geographically.
[Virtualization-based Security (VBS)](https://docs.microsoft.com/en-us/windows-hardware/design/device-experiences/oem-vbs) is a hardware virtualization feature to create and isolate a secure region of memory from the normal operating system.
[Hypervisor-Enforced Code Integrity (HVCI)](https://docs.microsoft.com/en-us/windows-hardware/drivers/bringup/device-guard-and-credential-guard) is a mechanism whereby a hypervisor, such as Hyper-V, uses hardware virtualization to protect kernel-mode processes against the injection and execution of malicious or unverified code. Code integrity validation is performed in a secure environment that is resistant to attack from malicious software, and page permissions for kernel mode are set and maintained by the hypervisor.
[KVM (for Kernel-based Virtual Machine)](https://www.linux-kvm.org/page/Main_Page) is a full virtualization solution for Linux on x86 hardware containing virtualization extensions (Intel VT or AMD-V). It consists of a loadable kernel module, kvm.ko, that provides the core virtualization infrastructure and a processor specific module, kvm-intel.ko or kvm-amd.ko.
[QEMU](https://www.qemu.org) is a fast processor emulator using a portable dynamic translator. QEMU emulates a full system, including a processor and various peripherals. It can be used to launch a different Operating System without rebooting the PC or to debug system code.
[Hyper-V](https://docs.microsoft.com/en-us/virtualization/hyper-v-on-windows/) enables running virtualized computer systems on top of a physical host. These virtualized systems can be used and managed just as if they were physical computer systems, however they exist in virtualized and isolated environment. Special software called a hypervisor manages access between the virtual systems and the physical hardware resources. Virtualization enables quick deployment of computer systems, a way to quickly restore systems to a previously known good state, and the ability to migrate systems between physical hosts.
[VirtManager](https://github.com/virt-manager/virt-manager) is a graphical tool for managing virtual machines via libvirt. Most usage is with QEMU/KVM virtual machines, but Xen and libvirt LXC containers are well supported. Common operations for any libvirt driver should work.
[oVirt](https://www.ovirt.org) is an open-source distributed virtualization solution, designed to manage your entire enterprise infrastructure. oVirt uses the trusted KVM hypervisor and is built upon several other community projects, including libvirt, Gluster, PatternFly, and Ansible.Founded by Red Hat as a community project on which Red Hat Enterprise Virtualization is based allowing for centralized management of virtual machines, compute, storage and networking resources, from an easy-to-use web-based front-end with platform independent access.
[HyperKit](https://github.com/moby/hyperkit) is a toolkit for embedding hypervisor capabilities in your application. It includes a complete hypervisor, based on [xhyve](https://github.com/mist64/xhyve)/[bhyve](https://bhyve.org/), which is optimized for lightweight virtual machines and container deployment. It is designed to be interfaced with higher-level components such as the [VPNKit](https://github.com/moby/vpnkit) and [DataKit](https://github.com/moby/datakit). HyperKit currently only supports macOS using the [Hypervisor.framework](https://developer.apple.com/library/mac/documentation/DriversKernelHardware/Reference/Hypervisor/index.html) making it a core component of Docker Desktop for Mac.
[Intel® Graphics Virtualization Technology (Intel® GVT)](https://github.com/intel/gvt-linux) is a full GPU virtualization solution with mediated pass-through, starting from 4th generation Intel Core (TM) processors with Intel processor graphics(Broadwell and newer). It can be used to virtualize the GPU for multiple guest virtual machines, effectively providing near-native graphics performance in the virtual machine and still letting your host use the virtualized GPU normally.
[Apple Hypervisor](https://developer.apple.com/documentation/hypervisor) is a frameowrk that builds virtualization solutions on top of a lightweight hypervisor, without third-party kernel extensions. Hypervisor provides C APIs so you can interact with virtualization technologies in user space, without writing kernel extensions (KEXTs). As a result, the apps you create using this framework are suitable for distribution on the [Mac App Store](https://www.appstore.com/).
[Apple Virtualization Framework](https://developer.apple.com/documentation/virtualization) is a framework that provides high-level APIs for creating and managing virtual machines on Apple silicon and Intel-based Mac computers. This framework is used to boot and run a Linux-based operating system in a custom environment that you define. It also supports the [Virtio specification](https://www.redhat.com/en/virtio-networking-series), which defines standard interfaces for many device types, including network, socket, serial port, storage, entropy, and memory-balloon devices.
[Apple Paravirtualized Graphics Framework](https://developer.apple.com/documentation/paravirtualizedgraphics) is a framework that implements hardware-accelerated graphics for macOS running in a virtual machine, hereafter known as the guest. The operating system provides a graphics driver that runs inside the guest, communicating with the framework in the host operating system to take advantage of Metal-accelerated graphics.
[Cloud Hypervisor](https://github.com/cloud-hypervisor/cloud-hypervisor) is an open source Virtual Machine Monitor (VMM) that runs on top of [KVM](https://www.kernel.org/doc/Documentation/virtual/kvm/api.txt). The project focuses on exclusively running modern, cloud workloads, on top of a limited set of hardware architectures and platforms. Cloud workloads refers to those that are usually run by customers inside a cloud provider. Cloud Hypervisor is implemented in [Rust](https://www.rust-lang.org/) and is based on the [rust-vmm](https://github.com/rust-vmm) crates.
[VMware vSphere Hypervisor](https://www.vmware.com/products/vsphere-hypervisor.html) is a bare-metal hypervisor that virtualizes servers; allowing you to consolidate your applications while saving time and money managing your IT infrastructure.
[Xen](https://github.com/xen-project/xen) is focused on advancing virtualization in a number of different commercial and open source applications, including server virtualization, Infrastructure as a Services (IaaS), desktop virtualization, security applications, embedded and hardware appliances, and automotive/aviation.
[Ganeti](https://github.com/ganeti/ganeti) is a virtual machine cluster management tool built on top of existing virtualization technologies such as Xen or KVM and other open source software. Once installed, the tool assumes management of the virtual instances (Xen DomU).
[Packer](https://www.packer.io/) is an open source tool for creating identical machine images for multiple platforms from a single source configuration. Packer is lightweight, runs on every major operating system, and is highly performant, creating machine images for multiple platforms in parallel. Packer does not replace configuration management like Chef or Puppet. In fact, when building images, Packer is able to use tools like Chef or Puppet to install software onto the image.
[Vagrant](https://www.vagrantup.com/) is a tool for building and managing virtual machine environments in a single workflow. With an easy-to-use workflow and focus on automation, Vagrant lowers development environment setup time, increases production parity, and makes the "works on my machine" excuse a relic of the past. It provides easy to configure, reproducible, and portable work environments built on top of industry-standard technology and controlled by a single consistent workflow to help maximize the productivity and flexibility of you and your team.
[Parallels Desktop](https://www.parallels.com) is a Desktop Hypervisor that delivers the fastest, easiest and most powerful application for running Windows/Linux on Mac (including the new [Apple M1 chip](https://www.apple.com/newsroom/2020/11/apple-unleashes-m1/)) and ChromeOS.
[VMware Fusion](https://www.vmware.com/products/fusion.html) is a Desktop Hypervisor that deliver desktop and ‘server’ virtual machines, containers and [Kubernetes clusters](https://www.vmware.com/topics/glossary/content/kubernetes-cluster) to developers, and IT professionals on the Mac.
[VMware Workstation](https://www.vmware.com/products/workstation-pro.html) is a hosted hypervisor that runs on x64 versions of Windows and Linux operating systems; it enables users to set up virtual machines on a single physical machine, and use them simultaneously along with the actual machine.
# Parallel Computing
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
## Parallel Computing Learning Resources
[Parallel Computing](https://en.wikipedia.org/wiki/Parallel_computing) is a computing environment in which two or more processors (cores, computers) work simultaneously to solve a single problem. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: [bit-level]https://en.wikipedia.org/wiki/Bit-level_parallelism), [instruction-level](https://en.wikipedia.org/wiki/Instruction-level_parallelism), [data](https://en.wikipedia.org/wiki/Data_parallelism), and [task parallelism](https://en.wikipedia.org/wiki/Task_parallelism).
[Accelerated Computing - Training | NVIDIA Developer](https://developer.nvidia.com/accelerated-computing-training)
[Fundamentals of Accelerated Computing with CUDA Python Course | NVIDIA](https://courses.nvidia.com/courses/course-v1:DLI+C-AC-02+V1/about)
[Top Parallel Computing Courses Online | Coursera](https://www.coursera.org/courses?languages=en&query=parallel%20computing)
[Top Parallel Computing Courses Online | Udemy](https://www.udemy.com/courses/search/?q=parallel+computation&src=sac&kw=parallel+comput)
[Scientific Computing Masterclass: Parallel and Distributed](https://www.udemy.com/course/learn-to-use-hpc-systems-and-supercomputers/)
[Learn Parallel Computing in Python | Udemy](https://www.udemy.com/course/parallel-computing-in-python/)
[GPU computing in Vulkan | Udemy](https://www.udemy.com/course/vulkan-gpu-computing/)
[High Performance Computing Courses | Udacity ](https://www.udacity.com/course/high-performance-computing--ud281)
[Parallel Computing Courses | Stanford Online](https://online.stanford.edu/courses/cs149-parallel-computing)
[Parallel Computing | MIT OpenCourseWare](https://ocw.mit.edu/courses/mathematics/18-337j-parallel-computing-fall-2011/)
[Multithreaded Parallelism: Languages and Compilers | MIT OpenCourseWare](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-827-multithreaded-parallelism-languages-and-compilers-fall-2002/)
[Parallel Computing with CUDA | Pluralsight](https://www.pluralsight.com/courses/parallel-computing-cuda)
[HPC Architecture and System Design | Intel](https://www.intel.com/content/www/us/en/high-performance-computing/hpc-architecture.html)
## Parallel Computing Tools, Libraries, and Frameworks
[MATLAB Parallel Server™](https://www.mathworks.com/products/matlab-parallel-server.html) is a tool that lets you scale MATLAB® programs and Simulink® simulations to clusters and clouds. You can prototype your programs and simulations on the desktop and then run them on clusters and clouds without recoding. MATLAB Parallel Server supports batch jobs, interactive parallel computations, and distributed computations with large matrices.
[Parallel Computing Toolbox™](https://www.mathworks.com/products/matlab-parallel-server.html) is a tool that lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
[Statistics and Machine Learning Toolbox™](https://www.mathworks.com/products/statistics.html) is a tool that provides functions and apps to describe, analyze, and model data. You can use descriptive statistics, visualizations, and clustering for exploratory data analysis; fit probability distributions to data; generate random numbers for Monte Carlo simulations, and perform hypothesis tests. Regression and classification algorithms let you draw inferences from data and build predictive models either interactively, using the Classification and Regression Learner apps, or programmatically, using AutoML.
[OpenMP](https://www.openmp.org/) is an API that supports multi-platform shared-memory parallel programming in C/C++ and Fortran. The OpenMP API defines a portable, scalable model with a simple and flexible interface for developing parallel applications on platforms from the desktop to the supercomputer.
[CUDA®](https://developer.nvidia.com/cuda-zone) is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs.
[Message Passing Interface (MPI)](https://en.wikipedia.org/wiki/Message_Passing_Interface) is a standardized and portable message-passing standard designed to function on parallel computing architectures.
[Microsoft MPI (MS-MPI)](https://docs.microsoft.com/en-us/message-passing-interface/microsoft-mpi) is a Microsoft implementation of the Message Passing Interface standard for developing and running parallel applications on the Windows platform.
[Slurm](https://researchcomputing.princeton.edu/support/knowledge-base/slurm) is a free open-source workload manager designed specifically to satisfy the demanding needs of high performance computing.
[Portable Batch System (PBS) Pro](https://www.altair.com/pbs-professional/) is a fast, powerful workload manager designed to improve productivity, optimize utilization and efficiency, and simplify administration for clusters, clouds, and supercomputers.
[AWS ParallelCluster](https://aws.amazon.com/hpc/parallelcluster/) is an AWS-supported open source cluster management tool that makes it easy for you to deploy and manage High Performance Computing (HPC) clusters on AWS. ParallelCluster uses a simple text file to model and provision all the resources needed for your HPC applications in an automated and secure manner.
[Numba](https://github.com/numba/numba) is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
[Chainer](https://chainer.org/) is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using [CuPy](https://github.com/cupy/cupy) for high performance training and inference.
[XGBoost](https://xgboost.readthedocs.io/) is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. It supports distributed training on multiple machines, including AWS, GCE, Azure, and Yarn clusters. Also, it can be integrated with Flink, Spark and other cloud dataflow systems.
[cuML](https://github.com/rapidsai/cuml) is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn.
[Apache Cassandra™](https://cassandra.apache.org/) is an open source NoSQL distributed database trusted by thousands of companies for scalability and high availability without compromising performance. Cassandra provides linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data.
[Apache Flume](https://flume.apache.org/) is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of streaming event data.
[Apache Mesos](http://mesos.apache.org/) is a cluster manager that provides efficient resource isolation and sharing across distributed applications, or frameworks. It can run Hadoop, Jenkins, Spark, Aurora, and other frameworks on a dynamically shared pool of nodes.
[Apache HBase™](https://hbase.apache.org/) is an open-source, NoSQL, distributed big data store. It enables random, strictly consistent, real-time access to petabytes of data. HBase is very effective for handling large, sparse datasets. HBase serves as a direct input and output to the Apache MapReduce framework for Hadoop, and works with Apache Phoenix to enable SQL-like queries over HBase tables.
[Hadoop Distributed File System (HDFS)](https://www.ibm.com/analytics/hadoop/hdfs) is a distributed file system that handles large data sets running on commodity hardware. It is used to scale a single Apache Hadoop cluster to hundreds (and even thousands) of nodes. HDFS is one of the major components of Apache Hadoop, the others being [MapReduce](https://www.ibm.com/analytics/hadoop/mapreduce) and [YARN](https://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html).
[Apache Arrow](https://arrow.apache.org/) is a language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs.
[Apache Spark™](https://spark.apache.org/) is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
[Apache PredictionIO](https://predictionio.apache.org/) is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.
[Microsoft Project Bonsai](https://azure.microsoft.com/en-us/services/project-bonsai/) is a low-code AI platform that speeds AI-powered automation development and part of the Autonomous Systems suite from Microsoft. Bonsai is used to build AI components that can provide operator guidance or make independent decisions to optimize process variables, improve production efficiency, and reduce downtime.
[Cluster Manager for Apache Kafka(CMAK)](https://github.com/yahoo/CMAK) is a tool for managing [Apache Kafka](https://kafka.apache.org/) clusters.
[BigDL](https://bigdl-project.github.io/) is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.
[Apache Cassandra™](https://cassandra.apache.org/) is an open source NoSQL distributed database trusted by thousands of companies for scalability and high availability without compromising performance. Cassandra provides linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data.
[Apache Flume](https://flume.apache.org/) is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of streaming event data.
[Apache Mesos](http://mesos.apache.org/) is a cluster manager that provides efficient resource isolation and sharing across distributed applications, or frameworks. It can run Hadoop, Jenkins, Spark, Aurora, and other frameworks on a dynamically shared pool of nodes.
[Apache Beam](https://beam.apache.org/) is an open source, unified model and set of language-specific SDKs for defining and executing data processing workflows, and also data ingestion and integration flows, supporting Enterprise Integration Patterns (EIPs) and Domain Specific Languages (DSLs).
[Jupyter Notebook](https://jupyter.org/) is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.
[Neo4j](https://neo4j.com/) is the only enterprise-strength graph database that combines native graph storage, advanced security, scalable speed-optimized architecture, and ACID compliance to ensure predictability and integrity of relationship-based queries.
[ElasticSearch](https://www.elastic.co/) is a search engine based on the Lucene library. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. Elasticsearch is developed in Java.
[Logstash](https://www.elastic.co/products/logstash) is a tool for managing events and logs. When used generically, the term encompasses a larger system of log collection, processing, storage and searching activities.
[Kibana](https://www.elastic.co/products/kibana) is an open source data visualization plugin for Elasticsearch. It provides visualization capabilities on top of the content indexed on an Elasticsearch cluster. Users can create bar, line and scatter plots, or pie charts and maps on top of large volumes of data.
[Trino](https://trino.io/) is a Distributed SQL query engine for big data. It is able to tremendously speed up [ETL processes](https://docs.microsoft.com/en-us/azure/architecture/data-guide/relational-data/etl), allow them all to use standard SQL statement, and work with numerous data sources and targets all in the same system.
[Extract, transform, and load (ETL)](https://docs.microsoft.com/en-us/azure/architecture/data-guide/relational-data/etl) is a data pipeline used to collect data from various sources, transform the data according to business rules, and load it into a destination data store.
[Redis(REmote DIctionary Server)](https://redis.io/) is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. It provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
[Apache OpenNLP](https://opennlp.apache.org/) is an open-source library for a machine learning based toolkit used in the processing of natural language text. It features an API for use cases like [Named Entity Recognition](https://en.wikipedia.org/wiki/Named-entity_recognition), [Sentence Detection](), [POS(Part-Of-Speech) tagging](https://en.wikipedia.org/wiki/Part-of-speech_tagging), [Tokenization](https://en.wikipedia.org/wiki/Tokenization_(data_security)) [Feature extraction](https://en.wikipedia.org/wiki/Feature_extraction), [Chunking](https://en.wikipedia.org/wiki/Chunking_(psychology)), [Parsing](https://en.wikipedia.org/wiki/Parsing), and [Coreference resolution](https://en.wikipedia.org/wiki/Coreference).
[Apache Airflow](https://airflow.apache.org) is an open-source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Install. Principles. Scalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
[Open Neural Network Exchange(ONNX)](https://github.com/onnx) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
[Apache MXNet](https://mxnet.apache.org/) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. Support for Python, R, Julia, Scala, Go, Javascript and more.
[AutoGluon](https://autogluon.mxnet.io/index.html) is toolkit for Deep learning that automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text data.
# OpenCL Development
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/130368400-7b6a82d3-ed03-4158-ade4-d7fc6cc9960a.png">
<br />
</p>
## OpenCL Learning Resources
[Open Computing Language (OpenCL)](https://www.khronos.org/opencl/) is an open standard for [parallel programming](https://www.coursera.org/lecture/parprog1/introduction-to-parallel-computing-zNrIS) of heterogeneous platforms consisting of CPUs, GPUs, and other hardware accelerators found in supercomputers, cloud servers, personal computers, mobile devices and embedded platforms.
[OpenCL | GitHub](https://github.com/OpenCL/)
[Khronos Group | GitHub](https://github.com/KhronosGroup/)
[Khronos Technology Courses and Training](https://www.khronos.org/developers/training/)
[OpenCL Tutorials - StreamHPC](https://streamhpc.com/knowledge/for-developers/tutorials/)
[Introduction to Intel® OpenCL Tools](https://software.intel.com/content/www/us/en/develop/articles/introduction-to-intel-opencl-tools.html)
[OpenCL | NVIDIA Developer](https://developer.nvidia.com/opencl)
[Introduction to OpenCL on FPGAs Course | Coursera](https://www.coursera.org/learn/opencl-fpga-introduction)
[Compiling OpenCL Kernel to FPGAs Course | Coursera](https://www.coursera.org/lecture/opencl-fpga-introduction/compiling-opencl-kernel-to-fpgas-g7MnU)
## OpenCL Tools, Libraries and Frameworks
[RenderDoc](https://renderdoc.org) is a stand-alone graphics debugger that allows quick and easy single-frame capture and detailed introspection of any application using Vulkan, D3D11, OpenGL & OpenGL ES or D3D12 across Windows, Linux, Android, Stadia, or Nintendo Switch™.
[GPUVerify](https://streamhpc.com/knowledge/tools/gpuverify/) is a tool for formal analysis of GPU kernels written in OpenCL and CUDA. The tool can prove that kernels are free from certain types of defect, including data races.
[OpenCL ICD Loader](https://github.com/KhronosGroup/OpenCL-ICD-Loader) is an Installable Client Driver (ICD) mechanism to allow developers to build applications against an Installable Client Driver loader (ICD loader) rather than linking their applications against a specific OpenCL implementation.
[clBLAS](https://github.com/clMathLibraries/clBLAS) is a software library containing BLAS functions written in OpenCL.
[clFFT](https://github.com/clMathLibraries/clFFT) is a software library containing FFT functions written in OpenCL.
[clSPARSE](https://github.com/clMathLibraries/clSPARSE) is a software library containing Sparse functions written in OpenCL.
[clRNG](https://github.com/clMathLibraries/clRNG) is an OpenCL based software library containing random number generation functions.
[CLsmith](https://github.com/ChrisLidbury/CLSmith/) is a tool that makes use of two existing testing techniques, Random Differential Testing and Equivalence Modulo Inputs (EMI), applying them in a many-core environment, OpenCL. Its primary feature is the generation of random OpenCL kernels, exercising many features of the language. It also brings a novel idea of applying EMI, via dead-code injection.
[Oclgrind](https://github.com/jrprice/Oclgrind) is a virtual OpenCL device simulator, including an OpenCL runtime with ICD support. The goal is to provide a platform for creating tools to aid OpenCL development. In particular, this project currently implements utilities for debugging memory access errors, detecting data-races and barrier divergence, collecting instruction histograms, and for interactive OpenCL kernel debugging. The simulator is built on an interpreter for LLVM IR.
[NVIDIA® Nsight™ Visual Studio Edition](https://developer.nvidia.com/nsight-visual-studio-edition) is an application development environment for heterogeneous platforms which brings GPU computing into Microsoft Visual Studio. NVIDIA Nsight™ VSE allows you to build and debug integrated GPU kernels and native CPU code as well as inspect the state of the GPU and memory.
[Radeon™ GPU Profiler](https://gpuopen.com/rgp/) is a performance tool that can be used by developers to optimize DirectX®12, Vulkan® and OpenCL™ applications for AMD RDNA™ and GCN hardware.
[Radeon™ GPU Analyzer](https://gpuopen.com/rga/) is a compiler and code analysis tool for Vulkan®, DirectX®, OpenGL® and OpenCL™.
[AMD Radeon ProRender](https://www.amd.com/en/technologies/radeon-prorender) is a powerful physically-based rendering engine that enables creative professionals to produce stunningly photorealistic images on virtually any GPU, any CPU, and any OS in over a dozen leading digital content creation and CAD applications.
[NVIDIA Omniverse](https://developer.nvidia.com/nvidia-omniverse-platform) is a powerful, multi-GPU, real-time simulation and collaboration platform for 3D production pipelines based on Pixar's Universal Scene Description and NVIDIA RTX.
[Intel® SDK For OpenCL™ Applications](https://software.intel.com/content/www/us/en/develop/tools/opencl-sdk.html) is an offload compute-intensive workloads. Customize heterogeneous compute applications and accelerate performance with kernel-based programming.
[NVIDIA NGC](https://ngc.nvidia.com/) is a hub for GPU-optimized software for deep learning, machine learning, and high-performance computing (HPC) workloads.
[NVIDIA NGC Containers](https://www.nvidia.com/en-us/gpu-cloud/containers/) is a registry that provides researchers, data scientists, and developers with simple access to a comprehensive catalog of GPU-accelerated software for AI, machine learning and HPC. These containers take full advantage of NVIDIA GPUs on-premises and in the cloud.
[NVIDIA cuDNN](https://developer.nvidia.com/cudnn) is a GPU-accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).
[NVIDIA Container Toolkit](https://github.com/NVIDIA/nvidia-docker) is a collection of tools & libraries that allows users to build and run GPU accelerated Docker containers. The toolkit includes a container runtime [library](https://github.com/NVIDIA/libnvidia-container) and utilities to automatically configure containers to leverage NVIDIA GPUs.
# CUDA Development
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/94306481-e17b8f00-ff27-11ea-832f-c85374acb3b1.png">
<br />
</p>
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/117718735-55a23480-b191-11eb-874d-e690d09cd490.png">
<br />
</p>
**CUDA Toolkit. Source: [NVIDIA Developer CUDA](https://developer.nvidia.com/cuda-zone)**
## CUDA Learning Resources
[CUDA](https://developer.nvidia.com/cuda-zone) is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs. In GPU-accelerated applications, the sequential part of the workload runs on the CPU, which is optimized for single-threaded. The compute intensive portion of the application runs on thousands of GPU cores in parallel. When using CUDA, developers can program in popular languages such as C, C++, Fortran, Python and MATLAB.
[CUDA Toolkit Documentation](https://docs.nvidia.com/cuda/index.html)
[CUDA Quick Start Guide](https://docs.nvidia.com/cuda/cuda-quick-start-guide/index.html)
[CUDA on WSL](https://docs.nvidia.com/cuda/wsl-user-guide/index.html)
[CUDA GPU support for TensorFlow](https://www.tensorflow.org/install/gpu)
[NVIDIA Deep Learning cuDNN Documentation](https://docs.nvidia.com/deeplearning/cudnn/api/index.html)
[NVIDIA GPU Cloud Documentation](https://docs.nvidia.com/ngc/ngc-introduction/index.html)
[NVIDIA NGC](https://ngc.nvidia.com/) is a hub for GPU-optimized software for deep learning, machine learning, and high-performance computing (HPC) workloads.
[NVIDIA NGC Containers](https://www.nvidia.com/en-us/gpu-cloud/containers/) is a registry that provides researchers, data scientists, and developers with simple access to a comprehensive catalog of GPU-accelerated software for AI, machine learning and HPC. These containers take full advantage of NVIDIA GPUs on-premises and in the cloud.
## CUDA Tools Libraries, and Frameworks
[CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) is a collection of tools & libraries that provide a development environment for creating high performance GPU-accelerated applications. The CUDA Toolkit allows you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms and HPC supercomputers. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler, and a runtime library to build and deploy your application on major architectures including x86, Arm and POWER.
[NVIDIA cuDNN](https://developer.nvidia.com/cudnn) is a GPU-accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).
[CUDA-X HPC](https://www.nvidia.com/en-us/technologies/cuda-x/) is a collection of libraries, tools, compilers and APIs that help developers solve the world's most challenging problems. CUDA-X HPC includes highly tuned kernels essential for high-performance computing (HPC).
[NVIDIA Container Toolkit](https://github.com/NVIDIA/nvidia-docker) is a collection of tools & libraries that allows users to build and run GPU accelerated Docker containers. The toolkit includes a container runtime [library](https://github.com/NVIDIA/libnvidia-container) and utilities to automatically configure containers to leverage NVIDIA GPUs.
[Minkowski Engine](https://nvidia.github.io/MinkowskiEngine) is an auto-differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unpooling, and broadcasting operations for sparse tensors.
[CUTLASS](https://github.com/NVIDIA/cutlass) is a collection of CUDA C++ template abstractions for implementing high-performance matrix-multiplication (GEMM) at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS.
[CUB](https://github.com/NVIDIA/cub) is a cooperative primitives for CUDA C++ kernel authors.
[Tensorman](https://github.com/pop-os/tensorman) is a utility for easy management of Tensorflow containers by developed by [System76]( https://system76.com).Tensorman allows Tensorflow to operate in an isolated environment that is contained from the rest of the system. This virtual environment can operate independent of the base system, allowing you to use any version of Tensorflow on any version of a Linux distribution that supports the Docker runtime.
[Numba](https://github.com/numba/numba) is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
[Chainer](https://chainer.org/) is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using [CuPy](https://github.com/cupy/cupy) for high performance training and inference.
[CuPy](https://cupy.dev/) is an implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it. It supports a subset of numpy.ndarray interface.
[CatBoost](https://catboost.ai/) is a fast, scalable, high performance [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
[cuDF](https://rapids.ai/) is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data. cuDF provides a pandas-like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming.
[cuML](https://github.com/rapidsai/cuml) is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn.
[ArrayFire](https://arrayfire.com/) is a general-purpose library that simplifies the process of developing software that targets parallel and massively-parallel architectures including CPUs, GPUs, and other hardware acceleration devices.
[Thrust](https://github.com/NVIDIA/thrust) is a C++ parallel programming library which resembles the C++ Standard Library. Thrust's high-level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs.
[AresDB](https://eng.uber.com/aresdb/) is a GPU-powered real-time analytics storage and query engine. It features low query latency, high data freshness and highly efficient in-memory and on disk storage management.
[Arraymancer](https://mratsim.github.io/Arraymancer/) is a tensor (N-dimensional array) project in Nim. The main focus is providing a fast and ergonomic CPU, Cuda and OpenCL ndarray library on which to build a scientific computing ecosystem.
[Kintinuous](https://github.com/mp3guy/Kintinuous) is a real-time dense visual SLAM system capable of producing high quality globally consistent point and mesh reconstructions over hundreds of metres in real-time with only a low-cost commodity RGB-D sensor.
[GraphVite](https://graphvite.io/) is a general graph embedding engine, dedicated to high-speed and large-scale embedding learning in various applications.
# Algorithms
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
[Fuzzy logic](https://www.investopedia.com/terms/f/fuzzy-logic.asp) is a heuristic approach that allows for more advanced decision-tree processing and better integration with rules-based programming.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/123861872-858dce80-d8dc-11eb-9a2c-51205d1541e9.png">
<br />
</p>
**Architecture of a Fuzzy Logic System. Source: [ResearchGate](https://www.researchgate.net/figure/Architecture-of-a-fuzzy-logic-system_fig2_309452475)**
[Support Vector Machine (SVM)](https://web.stanford.edu/~hastie/MOOC-Slides/svm.pdf) is a supervised machine learning model that uses classification algorithms for two-group classification problems.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/123858065-ec5cb900-d8d7-11eb-81c5-c6a8feefa84f.png">
<br />
</p>
**Support Vector Machine (SVM). Source:[OpenClipArt](https://openclipart.org/detail/182977/svm-support-vector-machines)**
[Neural networks](https://www.ibm.com/cloud/learn/neural-networks) are a subset of machine learning and are at the heart of deep learning algorithms. The name/structure is inspired by the human brain copying the process that biological neurons/nodes signal to one another.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/123858036-e5ce4180-d8d7-11eb-8c52-43d7c7e6e3c4.png">
<br />
</p>
**Deep neural network. Source: [IBM](https://www.ibm.com/cloud/learn/neural-networks)**
[Convolutional Neural Networks (R-CNN)](https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks) is an object detection algorithm that first segments the image to find potential relevant bounding boxes and then run the detection algorithm to find most probable objects in those bounding boxes.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/123858026-e36be780-d8d7-11eb-9034-8859d6f09490.png">
<br />
</p>
**Convolutional Neural Networks. Source:[CS231n](https://cs231n.github.io/convolutional-networks/#conv)**
[Recurrent neural networks (RNNs)](https://www.ibm.com/cloud/learn/recurrent-neural-networks) is a type of artificial neural network which uses sequential data or time series data.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/123858062-ebc42280-d8d7-11eb-9252-97e058bda8bd.png">
<br />
</p>
**Recurrent Neural Networks. Source: [Slideteam](https://www.slideteam.net/recurrent-neural-networks-rnns-ppt-powerpoint-presentation-file-templates.html)**
[Multilayer Perceptrons (MLPs)](https://deepai.org/machine-learning-glossary-and-terms/multilayer-perceptron) is multi-layer neural networks composed of multiple layers of [perceptrons](https://en.wikipedia.org/wiki/Perceptron) with a threshold activation.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/123858053-e8c93200-d8d7-11eb-844c-60463ecf662c.png">
<br />
</p>
**Multilayer Perceptrons. Source: [DeepAI](https://deepai.org/machine-learning-glossary-and-terms/multilayer-perceptron)**
[Random forest](https://www.ibm.com/cloud/learn/random-forest) is a commonly-used machine learning algorithm, which combines the output of multiple decision trees to reach a single result. A decision tree in a forest cannot be pruned for sampling and therefore, prediction selection. Its ease of use and flexibility have fueled its adoption, as it handles both classification and regression problems.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124398881-fe21d000-dccc-11eb-8f5f-0a0730d85d55.png">
<br />
</p>
**Random forest. Source: [wikimedia](https://community.tibco.com/wiki/random-forest-template-tibco-spotfirer-wiki-page)**
[Decision trees](https://www.cs.cmu.edu/~bhiksha/courses/10-601/decisiontrees/) are tree-structured models for classification and regression.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124398883-ffeb9380-dccc-11eb-9adb-66729a353132.png">
<br />
</p>
***Decision Trees. Source: [CMU](http://www.cs.cmu.edu/~bhiksha/courses/10-601/decisiontrees/)*
[Naive Bayes](https://en.wikipedia.org/wiki/Naive_Bayes_classifier) is a machine learning algorithm that is used solved calssification problems. It's based on applying [Bayes' theorem](https://www.mathsisfun.com/data/bayes-theorem.html) with strong independence assumptions between the features.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/124398885-00842a00-dccd-11eb-89c1-bd4c1adbf305.png">
<br />
</p>
**Bayes' theorem. Source:[mathisfun](https://www.mathsisfun.com/data/bayes-theorem.html)**
# Machine Learning
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/134075212-b132056a-5980-4610-a141-dd0677b17b5f.png">
<br />
</p>
## Learning Resources for ML
[Machine Learning](https://www.ibm.com/cloud/learn/machine-learning) is a branch of artificial intelligence (AI) focused on building apps using algorithms that learn from data models and improve their accuracy over time without needing to be programmed.
[Machine Learning by Stanford University from Coursera](https://www.coursera.org/learn/machine-learning)
[AWS Training and Certification for Machine Learning (ML) Courses](https://aws.amazon.com/training/learning-paths/machine-learning/)
[Machine Learning Scholarship Program for Microsoft Azure from Udacity](https://www.udacity.com/scholarships/machine-learning-scholarship-microsoft-azure)
[Microsoft Certified: Azure Data Scientist Associate](https://docs.microsoft.com/en-us/learn/certifications/azure-data-scientist)
[Microsoft Certified: Azure AI Engineer Associate](https://docs.microsoft.com/en-us/learn/certifications/azure-ai-engineer)
[Azure Machine Learning training and deployment](https://docs.microsoft.com/en-us/azure/devops/pipelines/targets/azure-machine-learning)
[Learning Machine learning and artificial intelligence from Google Cloud Training](https://cloud.google.com/training/machinelearning-ai)
[Machine Learning Crash Course for Google Cloud](https://developers.google.com/machine-learning/crash-course/)
[JupyterLab](https://jupyterlab.readthedocs.io/)
[Scheduling Jupyter notebooks on Amazon SageMaker ephemeral instances](https://aws.amazon.com/blogs/machine-learning/scheduling-jupyter-notebooks-on-sagemaker-ephemeral-instances/)
[How to run Jupyter Notebooks in your Azure Machine Learning workspace](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-run-jupyter-notebooks)
[Machine Learning Courses Online from Udemy](https://www.udemy.com/topic/machine-learning/)
[Machine Learning Courses Online from Coursera](https://www.coursera.org/courses?query=machine%20learning&)
[Learn Machine Learning with Online Courses and Classes from edX](https://www.edx.org/learn/machine-learning)
## ML Frameworks, Libraries, and Tools
[TensorFlow](https://www.tensorflow.org) is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
[Keras](https://keras.io) is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
[PyTorch](https://pytorch.org) is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.
[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models.
[Azure Databricks](https://azure.microsoft.com/en-us/services/databricks/) is a fast and collaborative Apache Spark-based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
[Microsoft Cognitive Toolkit (CNTK)](https://docs.microsoft.com/en-us/cognitive-toolkit/) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
[Apple CoreML](https://developer.apple.com/documentation/coreml) is a framework that helps integrate machine learning models into your app. Core ML provides a unified representation for all models. Your app uses Core ML APIs and user data to make predictions, and to train or fine-tune models, all on the user's device. A model is the result of applying a machine learning algorithm to a set of training data. You use a model to make predictions based on new input data.
[Tensorflow_macOS](https://github.com/apple/tensorflow_macos) is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
[Apache OpenNLP](https://opennlp.apache.org/) is an open-source library for a machine learning based toolkit used in the processing of natural language text. It features an API for use cases like [Named Entity Recognition](https://en.wikipedia.org/wiki/Named-entity_recognition), [Sentence Detection](), [POS(Part-Of-Speech) tagging](https://en.wikipedia.org/wiki/Part-of-speech_tagging), [Tokenization](https://en.wikipedia.org/wiki/Tokenization_(data_security)) [Feature extraction](https://en.wikipedia.org/wiki/Feature_extraction), [Chunking](https://en.wikipedia.org/wiki/Chunking_(psychology)), [Parsing](https://en.wikipedia.org/wiki/Parsing), and [Coreference resolution](https://en.wikipedia.org/wiki/Coreference).
[Apache Airflow](https://airflow.apache.org) is an open-source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Install. Principles. Scalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
[Open Neural Network Exchange(ONNX)](https://github.com/onnx) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
[Apache MXNet](https://mxnet.apache.org/) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. Support for Python, R, Julia, Scala, Go, Javascript and more.
[AutoGluon](https://autogluon.mxnet.io/index.html) is toolkit for Deep learning that automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text data.
[Anaconda](https://www.anaconda.com/) is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.
[PlaidML](https://github.com/plaidml/plaidml) is an advanced and portable tensor compiler for enabling deep learning on laptops, embedded devices, or other devices where the available computing hardware is not well supported or the available software stack contains unpalatable license restrictions.
[OpenCV](https://opencv.org) is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
[Scikit-Learn](https://scikit-learn.org/stable/index.html) is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.
[Weka](https://www.cs.waikato.ac.nz/ml/weka/) is an open source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or a Java API. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.
[Caffe](https://github.com/BVLC/caffe) is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.
[Theano](https://github.com/Theano/Theano) is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently including tight integration with NumPy.
[nGraph](https://github.com/NervanaSystems/ngraph) is an open source C++ library, compiler and runtime for Deep Learning. The nGraph Compiler aims to accelerate developing AI workloads using any deep learning framework and deploying to a variety of hardware targets.It provides the freedom, performance, and ease-of-use to AI developers.
[NVIDIA cuDNN](https://developer.nvidia.com/cudnn) is a GPU-accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).
[Jupyter Notebook](https://jupyter.org/) is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.
[Apache Spark](https://spark.apache.org/) is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
[Apache Spark Connector for SQL Server and Azure SQL](https://github.com/microsoft/sql-spark-connector) is a high-performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
[Apache PredictionIO](https://predictionio.apache.org/) is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.
[Cluster Manager for Apache Kafka(CMAK)](https://github.com/yahoo/CMAK) is a tool for managing [Apache Kafka](https://kafka.apache.org/) clusters.
[BigDL](https://bigdl-project.github.io/) is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.
[Eclipse Deeplearning4J (DL4J)](https://deeplearning4j.konduit.ai/) is a set of projects intended to support all the needs of a JVM-based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
[Tensorman](https://github.com/pop-os/tensorman) is a utility for easy management of Tensorflow containers by developed by [System76]( https://system76.com).Tensorman allows Tensorflow to operate in an isolated environment that is contained from the rest of the system. This virtual environment can operate independent of the base system, allowing you to use any version of Tensorflow on any version of a Linux distribution that supports the Docker runtime.
[Numba](https://github.com/numba/numba) is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
[Chainer](https://chainer.org/) is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using [CuPy](https://github.com/cupy/cupy) for high performance training and inference.
[XGBoost](https://xgboost.readthedocs.io/) is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. It supports distributed training on multiple machines, including AWS, GCE, Azure, and Yarn clusters. Also, it can be integrated with Flink, Spark and other cloud dataflow systems.
[cuML](https://github.com/rapidsai/cuml) is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn.
# Deep Learning Development
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/133943699-6dcfcb40-ddf7-4501-86e0-41e8aee91fe2.png">
<br />
</p>
## Deep Learning Learning Resources
[Deep Learning](https://www.ibm.com/cloud/learn/deep-learning) is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain,though, far from matching its ability. This allows the neural networks to "learn" from large amounts of data. The Learning can be [supervised](https://en.wikipedia.org/wiki/Supervised_learning), [semi-supervised](https://en.wikipedia.org/wiki/Semi-supervised_learning) or [unsupervised](https://en.wikipedia.org/wiki/Unsupervised_learning).
[Deep Learning Online Courses | NVIDIA](https://www.nvidia.com/en-us/training/online/)
[Top Deep Learning Courses Online | Coursera](https://www.coursera.org/courses?query=deep%20learning)
[Top Deep Learning Courses Online | Udemy](https://www.udemy.com/topic/deep-learning/)
[Learn Deep Learning with Online Courses and Lessons | edX](https://www.edx.org/learn/deep-learning)
[Deep Learning Online Course Nanodegree | Udacity](https://www.udacity.com/course/deep-learning-nanodegree--nd101)
[Machine Learning Course by Andrew Ng | Coursera](https://www.coursera.org/learn/machine-learning?)
[Machine Learning Engineering for Production (MLOps) course by Andrew Ng | Coursera](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops)
[Data Science: Deep Learning and Neural Networks in Python | Udemy](https://www.udemy.com/course/data-science-deep-learning-in-python/)
[Understanding Machine Learning with Python | Pluralsight ](https://www.pluralsight.com/courses/python-understanding-machine-learning)
[How to Think About Machine Learning Algorithms | Pluralsight](https://www.pluralsight.com/courses/machine-learning-algorithms)
[Deep Learning Courses | Stanford Online](https://online.stanford.edu/courses/cs230-deep-learning)
[Deep Learning - UW Professional & Continuing Education](https://www.pce.uw.edu/courses/deep-learning)
[Deep Learning Online Courses | Harvard University](https://online-learning.harvard.edu/course/deep-learning-0)
[Machine Learning for Everyone Courses | DataCamp](https://www.datacamp.com/courses/introduction-to-machine-learning-with-r)
[Artificial Intelligence Expert Course: Platinum Edition | Udemy](https://www.udemy.com/course/artificial-intelligence-exposed-future-10-extreme-edition/)
[Top Artificial Intelligence Courses Online | Coursera](https://www.coursera.org/courses?query=artificial%20intelligence)
[Learn Artificial Intelligence with Online Courses and Lessons | edX](https://www.edx.org/learn/artificial-intelligence)
[Professional Certificate in Computer Science for Artificial Intelligence | edX](https://www.edx.org/professional-certificate/harvardx-computer-science-for-artifical-intelligence)
[Artificial Intelligence Nanodegree program](https://www.udacity.com/course/ai-artificial-intelligence-nanodegree--nd898)
[Artificial Intelligence (AI) Online Courses | Udacity](https://www.udacity.com/school-of-ai)
[Intro to Artificial Intelligence Course | Udacity](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271)
[Edge AI for IoT Developers Course | Udacity](https://www.udacity.com/course/intel-edge-ai-for-iot-developers-nanodegree--nd131)
[Reasoning: Goal Trees and Rule-Based Expert Systems | MIT OpenCourseWare](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/lecture-3-reasoning-goal-trees-and-rule-based-expert-systems/)
[Expert Systems and Applied Artificial Intelligence](https://www.umsl.edu/~joshik/msis480/chapt11.htm)
[Autonomous Systems - Microsoft AI](https://www.microsoft.com/en-us/ai/autonomous-systems)
[Introduction to Microsoft Project Bonsai](https://docs.microsoft.com/en-us/learn/autonomous-systems/intro-to-project-bonsai/)
[Machine teaching with the Microsoft Autonomous Systems platform](https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/autonomous-systems)
[Autonomous Maritime Systems Training | AMC Search](https://www.amcsearch.com.au/ams-training)
[Top Autonomous Cars Courses Online | Udemy](https://www.udemy.com/topic/autonomous-cars/)
[Applied Control Systems 1: autonomous cars: Math + PID + MPC | Udemy](https://www.udemy.com/course/applied-systems-control-for-engineers-modelling-pid-mpc/)
[Learn Autonomous Robotics with Online Courses and Lessons | edX](https://www.edx.org/learn/autonomous-robotics)
[Artificial Intelligence Nanodegree program](https://www.udacity.com/course/ai-artificial-intelligence-nanodegree--nd898)
[Autonomous Systems Online Courses & Programs | Udacity](https://www.udacity.com/school-of-autonomous-systems)
[Edge AI for IoT Developers Course | Udacity](https://www.udacity.com/course/intel-edge-ai-for-iot-developers-nanodegree--nd131)
[Autonomous Systems MOOC and Free Online Courses | MOOC List](https://www.mooc-list.com/tags/autonomous-systems)
[Robotics and Autonomous Systems Graduate Program | Standford Online](https://online.stanford.edu/programs/robotics-and-autonomous-systems-graduate-program)
[Mobile Autonomous Systems Laboratory | MIT OpenCourseWare](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-186-mobile-autonomous-systems-laboratory-january-iap-2005/lecture-notes/)
## Deep Learning Tools, Libraries, and Frameworks
[NVIDIA cuDNN](https://developer.nvidia.com/cudnn) is a GPU-accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).
[NVIDIA DLSS (Deep Learning Super Sampling)](https://developer.nvidia.com/dlss) is a temporal image upscaling AI rendering technology that increases graphics performance using dedicated Tensor Core AI processors on GeForce RTX™ GPUs. DLSS uses the power of a deep learning neural network to boost frame rates and generate beautiful, sharp images for your games.
[AMD FidelityFX Super Resolution (FSR)](https://www.amd.com/en/technologies/radeon-software-fidelityfx) is an open source, high-quality solution for producing high resolution frames from lower resolution inputs. It uses a collection of cutting-edge Deep Learning algorithms with a particular emphasis on creating high-quality edges, giving large performance improvements compared to rendering at native resolution directly. FSR enables “practical performance” for costly render operations, such as hardware ray tracing for the AMD RDNA™ and AMD RDNA™ 2 architectures.
[Intel Xe Super Sampling (XeSS)](https://www.youtube.com/watch?v=Y9hfpf-SqEg) is a temporal image upscaling AI rendering technology that increases graphics performance similar to NVIDIA's [DLSS (Deep Learning Super Sampling)](https://developer.nvidia.com/dlss). Intel's Arc GPU architecture (early 2022) will have GPUs that feature dedicated Xe-cores to run XeSS. The GPUs will have Xe Matrix eXtenstions matrix (XMX) engines for hardware-accelerated AI processing. XeSS will be able to run on devices without XMX, including integrated graphics, though, the performance of XeSS will be lower on non-Intel graphics cards because it will be powered by [DP4a instruction](https://www.intel.com/content/dam/www/public/us/en/documents/reference-guides/11th-gen-quick-reference-guide.pdf).
[Jupyter Notebook](https://jupyter.org/) is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.
[Apache Spark](https://spark.apache.org/) is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
[Apache Spark Connector for SQL Server and Azure SQL](https://github.com/microsoft/sql-spark-connector) is a high-performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
[Apache PredictionIO](https://predictionio.apache.org/) is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.
[Cluster Manager for Apache Kafka(CMAK)](https://github.com/yahoo/CMAK) is a tool for managing [Apache Kafka](https://kafka.apache.org/) clusters.
[BigDL](https://bigdl-project.github.io/) is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.
[Eclipse Deeplearning4J (DL4J)](https://deeplearning4j.konduit.ai/) is a set of projects intended to support all the needs of a JVM-based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
[Deep Learning Toolbox™](https://www.mathworks.com/products/deep-learning.html) is a tool that provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
[Reinforcement Learning Toolbox™](https://www.mathworks.com/products/reinforcement-learning.html) is a tool that provides an app, functions, and a Simulink® block for training policies using reinforcement learning algorithms, including DQN, PPO, SAC, and DDPG. You can use these policies to implement controllers and decision-making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
[Deep Learning HDL Toolbox™](https://www.mathworks.com/products/deep-learning-hdl.html) is a tool that provides functions and tools to prototype and implement deep learning networks on FPGAs and SoCs. It provides pre-built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
[Parallel Computing Toolbox™](https://www.mathworks.com/products/matlab-parallel-server.html) is a tool that lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
[XGBoost](https://xgboost.readthedocs.io/) is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. It supports distributed training on multiple machines, including AWS, GCE, Azure, and Yarn clusters. Also, it can be integrated with Flink, Spark and other cloud dataflow systems.
[LIBSVM](https://www.csie.ntu.edu.tw/~cjlin/libsvm/) is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
[Scikit-Learn](https://scikit-learn.org/stable/index.html) is a simple and efficient tool for data mining and data analysis. It is built on NumPy,SciPy, and mathplotlib.
[TensorFlow](https://www.tensorflow.org) is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
[Keras](https://keras.io) is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
[PyTorch](https://pytorch.org) is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.
[Azure Databricks](https://azure.microsoft.com/en-us/services/databricks/) is a fast and collaborative Apache Spark-based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
[Microsoft Cognitive Toolkit (CNTK)](https://docs.microsoft.com/en-us/cognitive-toolkit/) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
[Tensorflow_macOS](https://github.com/apple/tensorflow_macos) is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
[Apache Airflow](https://airflow.apache.org) is an open-source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Install. Principles. Scalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
[Open Neural Network Exchange(ONNX)](https://github.com/onnx) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
[Apache MXNet](https://mxnet.apache.org/) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. Support for Python, R, Julia, Scala, Go, Javascript and more.
[AutoGluon](https://autogluon.mxnet.io/index.html) is toolkit for Deep learning that automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text data.
[Anaconda](https://www.anaconda.com/) is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.
[PlaidML](https://github.com/plaidml/plaidml) is an advanced and portable tensor compiler for enabling deep learning on laptops, embedded devices, or other devices where the available computing hardware is not well supported or the available software stack contains unpalatable license restrictions.
[OpenCV](https://opencv.org) is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
[Scikit-Learn](https://scikit-learn.org/stable/index.html) is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.
[Weka](https://www.cs.waikato.ac.nz/ml/weka/) is an open source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or a Java API. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.
[Caffe](https://github.com/BVLC/caffe) is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.
[Theano](https://github.com/Theano/Theano) is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently including tight integration with NumPy.
[Microsoft Project Bonsai](https://azure.microsoft.com/en-us/services/project-bonsai/) is a low-code AI platform that speeds AI-powered automation development and part of the Autonomous Systems suite from Microsoft. Bonsai is used to build AI components that can provide operator guidance or make independent decisions to optimize process variables, improve production efficiency, and reduce downtime.
[Microsoft AirSim](https://microsoft.github.io/AirSim/lidar.html) is a simulator for drones, cars and more, built on Unreal Engine (with an experimental Unity release). AirSim is open-source, cross platform, and supports [software-in-the-loop simulation](https://www.mathworks.com/help///ecoder/software-in-the-loop-sil-simulation.html) with popular flight controllers such as PX4 & ArduPilot and [hardware-in-loop](https://www.ni.com/en-us/innovations/white-papers/17/what-is-hardware-in-the-loop-.html) with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.
[CARLA](https://github.com/carla-simulator/carla) is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely.
[ROS/ROS2 bridge for CARLA(package)](https://github.com/carla-simulator/ros-bridge) is a bridge that enables two-way communication between ROS and CARLA. The information from the CARLA server is translated to ROS topics. In the same way, the messages sent between nodes in ROS get translated to commands to be applied in CARLA.
[ROS Toolbox](https://www.mathworks.com/products/ros.html) is a tool that provides an interface connecting MATLAB® and Simulink® with the Robot Operating System (ROS and ROS 2), enabling you to create a network of ROS nodes. The toolbox includes MATLAB functions and Simulink blocks to import, analyze, and play back ROS data recorded in rosbag files. You can also connect to a live ROS network to access ROS messages.
[Robotics Toolbox™](https://www.mathworks.com/products/robotics.html) provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
[Image Processing Toolbox™](https://www.mathworks.com/products/image.html) is a tool that provides a comprehensive set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
[Computer Vision Toolbox™](https://www.mathworks.com/products/computer-vision.html) is a tool that provides algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. You can perform object detection and tracking, as well as feature detection, extraction, and matching. You can automate calibration workflows for single, stereo, and fisheye cameras. For 3D vision, the toolbox supports visual and point cloud SLAM, stereo vision, structure from motion, and point cloud processing.
[Robotics Toolbox™](https://www.mathworks.com/products/robotics.html) is a tool that provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
[Model Predictive Control Toolbox™](https://www.mathworks.com/products/model-predictive-control.html) is a tool that provides functions, an app, and Simulink® blocks for designing and simulating controllers using linear and nonlinear model predictive control (MPC). The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. By running closed-loop simulations, you can evaluate controller performance.
[Predictive Maintenance Toolbox™](https://www.mathworks.com/products/predictive-maintenance.html) is a tool that lets you manage sensor data, design condition indicators, and estimate the remaining useful life (RUL) of a machine. The toolbox provides functions and an interactive app for exploring, extracting, and ranking features using data-based and model-based techniques, including statistical, spectral, and time-series analysis.
[Vision HDL Toolbox™](https://www.mathworks.com/products/vision-hdl.html) is a tool that provides pixel-streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
[Automated Driving Toolbox™](https://www.mathworks.com/products/automated-driving.html) is a MATLAB tool that provides algorithms and tools for designing, simulating, and testing ADAS and autonomous driving systems. You can design and test vision and lidar perception systems, as well as sensor fusion, path planning, and vehicle controllers. Visualization tools include a bird’s-eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.
[UAV Toolbox](https://www.mathworks.com/products/uav.html) is an application that provides tools and reference applications for designing, simulating, testing, and deploying unmanned aerial vehicle (UAV) and drone applications. You can design autonomous flight algorithms, UAV missions, and flight controllers. The Flight Log Analyzer app lets you interactively analyze 3D flight paths, telemetry information, and sensor readings from common flight log formats.
[Navigation Toolbox™](https://www.mathworks.com/products/navigation.html) is a tool that provides algorithms and analysis tools for motion planning, simultaneous localization and mapping (SLAM), and inertial navigation. The toolbox includes customizable search and sampling-based path planners, as well as metrics for validating and comparing paths. You can create 2D and 3D map representations, generate maps using SLAM algorithms, and interactively visualize and debug map generation with the SLAM map builder app.
[Lidar Toolbox™](https://www.mathworks.com/products/lidar.html) is a tool that provides algorithms, functions, and apps for designing, analyzing, and testing lidar processing systems. You can perform object detection and tracking, semantic segmentation, shape fitting, lidar registration, and obstacle detection. Lidar Toolbox supports lidar-camera cross calibration for workflows that combine computer vision and lidar processing.
[Mapping Toolbox™](https://www.mathworks.com/products/mapping.html) is a tool that provides algorithms and functions for transforming geographic data and creating map displays. You can visualize your data in a geographic context, build map displays from more than 60 map projections, and transform data from a variety of sources into a consistent geographic coordinate system.
# Computer Vision Development
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/129494417-b0ee8192-ac41-4a6d-8e1d-4761ffc8bab1.png">
<br />
</p>
## Computer Vision Learning Resources
[Computer Vision](https://azure.microsoft.com/en-us/overview/what-is-computer-vision/) is a field of Artificial Intelligence (AI) that focuses on enabling computers to identify and understand objects and people in images and videos.
[OpenCV Courses](https://opencv.org/courses/)
[Exploring Computer Vision in Microsoft Azure](https://docs.microsoft.com/en-us/learn/paths/explore-computer-vision-microsoft-azure/)
[Top Computer Vision Courses Online | Coursera](https://www.coursera.org/courses?languages=en&query=computer%20vision)
[Top Computer Vision Courses Online | Udemy](https://www.udemy.com/topic/computer-vision/)
[Learn Computer Vision with Online Courses and Lessons | edX](https://www.edx.org/learn/computer-vision)
[Computer Vision and Image Processing Fundamentals | edX](https://www.edx.org/course/computer-vision-and-image-processing-fundamentals)
[Introduction to Computer Vision Courses | Udacity](https://www.udacity.com/course/introduction-to-computer-vision--ud810)
[Computer Vision Nanodegree program | Udacity](https://www.udacity.com/course/computer-vision-nanodegree--nd891)
[Machine Vision Course |MIT Open Courseware ](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-801-machine-vision-fall-2004/)
[Computer Vision Training Courses | NobleProg](https://www.nobleprog.com/computer-vision-training)
[Visual Computing Graduate Program | Stanford Online](https://online.stanford.edu/programs/visual-computing-graduate-program)
## Computer Vision Tools, Libraries, and Frameworks
[OpenCV](https://opencv.org) is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
[Microsoft Cognitive Toolkit (CNTK)](https://docs.microsoft.com/en-us/cognitive-toolkit/) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
[Scikit-Learn](https://scikit-learn.org/stable/index.html) is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.
[NVIDIA cuDNN](https://developer.nvidia.com/cudnn) is a GPU-accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).
[Automated Driving Toolbox™](https://www.mathworks.com/products/automated-driving.html) is a MATLAB tool that provides algorithms and tools for designing, simulating, and testing ADAS and autonomous driving systems. You can design and test vision and lidar perception systems, as well as sensor fusion, path planning, and vehicle controllers. Visualization tools include a bird’s-eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.
[LRSLibrary](https://github.com/andrewssobral/lrslibrary) is a Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos. The library was designed for moving object detection in videos, but it can be also used for other computer vision and machine learning problems.
[Image Processing Toolbox™](https://www.mathworks.com/products/image.html) is a tool that provides a comprehensive set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
[Computer Vision Toolbox™](https://www.mathworks.com/products/computer-vision.html) is a tool that provides algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. You can perform object detection and tracking, as well as feature detection, extraction, and matching. You can automate calibration workflows for single, stereo, and fisheye cameras. For 3D vision, the toolbox supports visual and point cloud SLAM, stereo vision, structure from motion, and point cloud processing.
[Statistics and Machine Learning Toolbox™](https://www.mathworks.com/products/statistics.html) is a tool that provides functions and apps to describe, analyze, and model data. You can use descriptive statistics, visualizations, and clustering for exploratory data analysis; fit probability distributions to data; generate random numbers for Monte Carlo simulations, and perform hypothesis tests. Regression and classification algorithms let you draw inferences from data and build predictive models either interactively, using the Classification and Regression Learner apps, or programmatically, using AutoML.
[Lidar Toolbox™](https://www.mathworks.com/products/lidar.html) is a tool that provides algorithms, functions, and apps for designing, analyzing, and testing lidar processing systems. You can perform object detection and tracking, semantic segmentation, shape fitting, lidar registration, and obstacle detection. Lidar Toolbox supports lidar-camera cross calibration for workflows that combine computer vision and lidar processing.
[Mapping Toolbox™](https://www.mathworks.com/products/mapping.html) is a tool that provides algorithms and functions for transforming geographic data and creating map displays. You can visualize your data in a geographic context, build map displays from more than 60 map projections, and transform data from a variety of sources into a consistent geographic coordinate system.
[UAV Toolbox](https://www.mathworks.com/products/uav.html) is an application that provides tools and reference applications for designing, simulating, testing, and deploying unmanned aerial vehicle (UAV) and drone applications. You can design autonomous flight algorithms, UAV missions, and flight controllers. The Flight Log Analyzer app lets you interactively analyze 3D flight paths, telemetry information, and sensor readings from common flight log formats.
[Parallel Computing Toolbox™](https://www.mathworks.com/products/matlab-parallel-server.html) is a tool that lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
[Partial Differential Equation Toolbox™](https://www.mathworks.com/products/pde.html) is a tool that provides functions for solving structural mechanics, heat transfer, and general partial differential equations (PDEs) using finite element analysis.
[ROS Toolbox](https://www.mathworks.com/products/ros.html) is a tool that provides an interface connecting MATLAB® and Simulink® with the Robot Operating System (ROS and ROS 2), enabling you to create a network of ROS nodes. The toolbox includes MATLAB functions and Simulink blocks to import, analyze, and play back ROS data recorded in rosbag files. You can also connect to a live ROS network to access ROS messages.
[Robotics Toolbox™](https://www.mathworks.com/products/robotics.html) provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
[Deep Learning Toolbox™](https://www.mathworks.com/products/deep-learning.html) is a tool that provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
[Reinforcement Learning Toolbox™](https://www.mathworks.com/products/reinforcement-learning.html) is a tool that provides an app, functions, and a Simulink® block for training policies using reinforcement learning algorithms, including DQN, PPO, SAC, and DDPG. You can use these policies to implement controllers and decision-making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
[Deep Learning HDL Toolbox™](https://www.mathworks.com/products/deep-learning-hdl.html) is a tool that provides functions and tools to prototype and implement deep learning networks on FPGAs and SoCs. It provides pre-built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
[Model Predictive Control Toolbox™](https://www.mathworks.com/products/model-predictive-control.html) is a tool that provides functions, an app, and Simulink® blocks for designing and simulating controllers using linear and nonlinear model predictive control (MPC). The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. By running closed-loop simulations, you can evaluate controller performance.
[Vision HDL Toolbox™](https://www.mathworks.com/products/vision-hdl.html) is a tool that provides pixel-streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
[Data Acquisition Toolbox™](https://www.mathworks.com/products/data-acquisition.html) is a tool that provides apps and functions for configuring data acquisition hardware, reading data into MATLAB® and Simulink®, and writing data to DAQ analog and digital output channels. The toolbox supports a variety of DAQ hardware, including USB, PCI, PCI Express®, PXI®, and PXI Express® devices, from National Instruments® and other vendors.
[Microsoft AirSim](https://microsoft.github.io/AirSim/lidar.html) is a simulator for drones, cars and more, built on Unreal Engine (with an experimental Unity release). AirSim is open-source, cross platform, and supports [software-in-the-loop simulation](https://www.mathworks.com/help///ecoder/software-in-the-loop-sil-simulation.html) with popular flight controllers such as PX4 & ArduPilot and [hardware-in-loop](https://www.ni.com/en-us/innovations/white-papers/17/what-is-hardware-in-the-loop-.html) with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.
# Gaming
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
## Performance Benchmarks
[Geekbench 5](https://www.geekbench.com/download/) is a cross-platform benchmark that measures your system's performance with the press of a button.
[Phoronix Test Suite](https://www.phoronix-test-suite.com/)
[UNIGINE Superposition](https://benchmark.unigine.com/superposition) is an extreme performance and stability test for PC hardware: video card, power supply, cooling system.
<img src="https://user-images.githubusercontent.com/45159366/107092007-8f8d2480-67b7-11eb-9c3f-a0cb02e6dfcd.png">
## Apple Arcade
[Apple Arcade](https://www.apple.com/apple-arcade/) is a game subscription service that gives up to six family members unlimited access to 100+ incredibly fun games, all with no ads and no in-app purchases.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/112693074-377cb100-8e3d-11eb-910c-1095c91da6d5.png">
</p>
## Steam
[Get Steam](https://store.steampowered.com/about/)
<img src="https://user-images.githubusercontent.com/45159366/106686402-13100100-657f-11eb-9012-6bdac264a808.png">
[Steam Link app](https://store.steampowered.com/steamlink/about) is available free of charge, streaming your Steam PC games to phones, tablets, and TV.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/112692999-14ea9800-8e3d-11eb-964a-6bee4e665900.png">
</p>
## Game Streaming
[Geforce NOW](https://www.nvidia.com/en-us/geforce-now/download/) is NVIDIA's Cloud Gaming Service.
<img src="https://user-images.githubusercontent.com/45159366/106686391-0f7c7a00-657f-11eb-9d0b-1ebb4d385883.jpeg">
[Moonlight Game Streaming](https://moonlight-stream.org/) is a program that let you stream from your PC games over the Internet with no configuration required. Stream from almost any device, whether you're in another room or miles away from your gaming rig.
<img src="https://user-images.githubusercontent.com/45159366/106686398-11463d80-657f-11eb-841a-d534829ccc3d.png">
[Chiaki](https://git.sr.ht/~thestr4ng3r/chiaki) is a Free and Open Source Software Client for PlayStation 4 and PlayStation 5 Remote Play for Linux, FreeBSD, OpenBSD, Android, macOS, Windows, Nintendo Switch and potentially even more platforms.
[Xbox Project xCloud](https://www.xbox.com/en-US/xbox-game-streaming/project-xcloud) is Microsoft's cloud-based Xbox game-streaming technology **(currently in Beta)**. **Play games like Forza Horizon 4, Halo 5: Guardians, Gears of War 4, Sea of Thieves, Cuphead, Red Dead Redemption 2, and 100+ other games on your mobile device or Chrome web browser**. Microsoft's Xbox Project xCloud does require an [Xbox Game Pass Ultimate](https://www.xbox.com/en-US/xbox-game-pass/cloud-gaming) subscription.
<img src="https://user-images.githubusercontent.com/45159366/108111388-74d56e00-7049-11eb-8aeb-3e5d65f9e832.png">
[Amazon Luna](https://www.amazon.com/luna/landing-page) is Amazon's Cloud Gaming Service. Amazon Luna is Compatible/Supported on a vartiey of [Devices and Browsers](https://www.amazon.com/gp/help/customer/display.html?nodeId=GUFHUSX8X324T4XE).
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/112693072-364b8400-8e3d-11eb-9df0-d58af7ac9c9c.png">
</p>
## Game Emulators
[RetroArch](https://www.retroarch.com/) is a frontend for emulators, game engines and media players. It enables you to run classic games on a wide range of computers and consoles through its slick graphical interface. Settings are also unified so configuration is done once and for all.
[Dolphin](https://dolphin-emu.org) is an emulator for two recent Nintendo video game consoles: the GameCube and the Wii. It allows PC gamers to enjoy games for these two consoles in full HD (1080p) with several enhancements: compatibility with all PC controllers, turbo speed, networked multiplayer, and even more.
[Citra](https://citra-emu.org/) is an open-source emulator for the Nintendo 3DS capable of playing many of your favorite games.
[yuzu](https://yuzu-emu.org) is an experimental open-source emulator for the Nintendo Switch from the creators of Citra.
[DOSBox](https://www.dosbox.com/) is an open-source DOS emulator which primarily focuses on running DOS Games.
[MAME](https://www.mamedev.org/) is a Arcade Machine Emulator.
[xemu](https://xemu.app/) is an original Xbox emulator.
# Game Development
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
<h1 align="center">
<img src="https://user-images.githubusercontent.com/45159366/97361059-45151700-185c-11eb-9d12-dae51c79eb8a.png">
<br />
</h1>
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/119279021-ea6b5000-bbdd-11eb-9f59-5251fc3ac751.png">
<br />
</p>
## Game Engines
[Unity](https://unity.com) is a cross-platform game development platform. Use Unity to build high-quality 3D and 2D games, deploy them across mobile, desktop, VR/AR, consoles or the Web, and connect with loyal and enthusiastic players and customers.
<img src="https://user-images.githubusercontent.com/45159366/104788113-3432be00-5746-11eb-99b1-49360669f327.png">
[Unreal Engine 4](https://www.unrealengine.com) is a game engine developed by Epic Games with the world's most open and advanced real-time 3D creation tool. Continuously evolving to serve not only its original purpose as a state-of-the-art game engine, today it gives creators across industries the freedom and control to deliver cutting-edge content, interactive experiences, and immersive virtual worlds.
<img src="https://user-images.githubusercontent.com/45159366/104788122-37c64500-5746-11eb-8f61-48a69b94582d.png">
[Godot Engine](https://godotengine.org) is a feature-packed, cross-platform game engine to create 2D and 3D games from a unified interface. It provides a comprehensive set of common tools, so that users can focus on making games without having to reinvent the wheel. Games can be exported in one click to a number of platforms, including the major desktop platforms (Linux, Mac OSX, Windows) as well as mobile (Android, iOS) and web-based (HTML5) platforms.
[If you would like to Donate to the Godot Project](https://www.patreon.com/godotengine)
<img src="https://user-images.githubusercontent.com/45159366/104788134-3f85e980-5746-11eb-94c4-d97165ee888b.jpeg">
[Blender](https://www.blender.org) is the free and open source 3D creation suite. It supports the entirety of the 3D pipeline—modeling, rigging, animation, simulation, rendering, compositing and motion tracking, video editing and 2D animation pipeline.
[If you would like to Donate to the Blender Project](https://fund.blender.org/)
<img src="https://user-images.githubusercontent.com/45159366/110990485-3ec59a00-8328-11eb-8eb1-5502eb2eb74e.png">
[Unigine](https://unigine.com) is a cross-platform game engine designed for development teams (C++/C# programmers, 3D artists) working on interactive 3D apps.
<img src="https://user-images.githubusercontent.com/45159366/110880414-70405600-8293-11eb-9c86-bb373017653a.png">
[GameMaker Studio 2](https://www.yoyogames.com/gamemaker) is the latest and greatest incarnation of GameMaker. It has everything you need to take your idea from concept to finished game. With no barriers to entry and powerful functionality, GameMaker Studio 2 is the ultimate 2D development environment.
<img src="https://user-images.githubusercontent.com/45159366/104788147-44e33400-5746-11eb-879a-bc6239c98ce4.jpg">
## Game Development Tools, Libraries, and Frameworks
[Panda3D](https://www.panda3d.org/) is a game engine, a framework for 3D rendering and game development for Python and C++ programs, developed by Disney and CMU. Panda3D is open-source and free for any purpose, including commercial ventures.
[Source 2](https://developer.valvesoftware.com/wiki/Source_2) is a 3D video game engine in development by Valve as a successor to Source. It is used in Dota 2, Artifact, Dota Underlords, parts of The Lab, SteamVR Home, and Half-Life: Alyx.
[AutoDesk 3ds Max](https://www.autodesk.com/products/3ds-max/overview) is a professional software program for 3D modeling, animation, rendering, and visualization. 3ds Max allows you to create stunning game environments, design visualizations, and virtual reality experiences.
[Houdini](https://www.sidefx.com/) is a 3D procedural software for modeling, rigging, animation, VFX, look development, lighting and rendering in film, TV, advertising and video game pipelines.
[Open Graphics Library(OpenGL)](https://www.opengl.org/) is an API used acrossed mulitple programming languages and platforms for hardware-accelerated rendering of 2D/3D vector graphics currently developed by the [Khronos Group](https://www.khronos.org/).
[Open Computing Language (OpenCL)](https://www.khronos.org/opencl/) is an open standard for [parallel programming](https://www.coursera.org/lecture/parprog1/introduction-to-parallel-computing-zNrIS) of heterogeneous platforms consisting of CPUs, GPUs, and other hardware accelerators found in supercomputers, cloud servers, personal computers, mobile devices and embedded platforms.
[OpenGL Shading Language(GLSL)](https://www.khronos.org/opengl/wiki/Core_Language_(GLSL)) is a High Level Shading Language based on the C-style language, so it covers most of the features a user would expect with such a language. Such as control structures (for-loops, if-else statements, etc) exist in GLSL, including the switch statement.
[High Level Shading Language(HLSL)](https://docs.microsoft.com/en-us/windows/win32/direct3dhlsl/dx-graphics-hlsl) is the High Level Shading Language for DirectX. Using HLSL, the user can create C-like programmable shaders for the Direct3D pipeline. HLSL was first created with DirectX 9 to set up the programmable 3D pipeline.
[Vulkan](https://www.khronos.org/vulkan/) is a modern cross-platform graphics and compute API that provides high-efficiency, cross-platform access to modern GPUs used in a wide variety of devices from PCs and consoles to mobile phones and embedded platforms. Vulkan is currently in development by the Khronos consortium.
[Metal](https://developer.apple.com/metal/) is a low-level GPU programming framework used for rendering 2D and 3D graphics on Apple platforms such as iOS, iPadOS, macOS, watchOS and tvOS.
[MoltenVK](https://moltengl.com/moltenvk) is an implementation of Vulkan running on iOS and macOS using Apple's [Metal](https://developer.apple.com/metal/) graphics framework.
[MoltenGL](https://moltengl.com) is an implementation of the OpenGL ES 2.0 API that runs on Apple's [Metal](https://developer.apple.com/metal/) graphics framework.
[AMD Radeon ProRender](https://www.amd.com/en/technologies/radeon-prorender) is a powerful physically-based rendering engine that enables creative professionals to produce stunningly photorealistic images on virtually any GPU, any CPU, and any OS in over a dozen leading digital content creation and CAD applications.
[NVIDIA Omniverse](https://developer.nvidia.com/nvidia-omniverse-platform) is a powerful, multi-GPU, real-time simulation and collaboration platform for 3D production pipelines based on Pixar's Universal Scene Description and NVIDIA RTX.
[LibGDX](https://github.com/libgdx/libgdx) is a cross-platform Java game development framework based on OpenGL (ES) that works on Windows, Linux, Mac OS X, Android, your WebGL enabled browser and iOS.
[cocos2d-x](https://github.com/cocos2d/cocos2d-x) is a multi-platform framework for building 2d games, interactive books, demos and other graphical applications. It is based on cocos2d-iphone, but instead of using Objective-C, it uses C++. It works on iOS, Android, macOS, Windows and Linux.
[MonoGame](https://github.com/MonoGame/MonoGame) is a framework for creating powerful cross-platform games. The spiritual successor to XNA with thousands of titles shipped across desktop, mobile, and console platforms. MonoGame is a fully managed .NET open source game framework without any black boxes.
[HGIG](https://www.hgig.org/) is a volunteer group of companies from the game and TV display industries that meet to specify and make available for the public guidelines to improve consumer gaming experiences in HDR.
[Three.js](https://threejs.org) is a cross-browser JavaScript library and application programming interface used to create and display animated 3D computer graphics in a web browser using WebGL.
[Superpowers](http://superpowers-html5.com/) is a downloadable HTML5 app for real-time collaborative projects . You can use it solo like a regular offline game maker, or setup a password and let friends join in on your project through their Web browser.
[Vivox](https://www.vivox.com/) is a voice & text chat platform that's trusted by the world's biggest gaming brands and titles such as Fortnite, PUBG, League of Legends, and Rainbow Six Siege.
[HGIG](https://www.hgig.org/) is a volunteer group of companies from the game and TV display industries that meet to specify and make available for the public guidelines to improve consumer gaming experiences in HDR.
[GameBlocks](https://www.gameblocks.com/) is a Server Side Anti-Cheat & Middleware software.
## Augmented Reality (AR) & Virtual Reality (VR)
[ARKit](https://developer.apple.com/augmented-reality/arkit/) is a set set of software development tools to enable developers to build augmented-reality apps for iOS developed by Apple. The latest version ARKit 3.5 takes advantage of the new LiDAR Scanner and depth sensing system on iPad Pro(2020) to support a new generation of AR apps that use Scene Geometry for enhanced scene understanding and object occlusion.
[RealityKit](https://developer.apple.com/documentation/realitykit) is a framework to implement high-performance 3D simulation and rendering with information provided by the ARKit framework to seamlessly integrate virtual objects into the real world.
[SceneKit](https://developer.apple.com/scenekit/) is a high-level 3D graphics framework that helps you create 3D animated scenes and effects in your iOS apps.
[ARCore](https://developers.google.com/ar/) is a software development kit developed by Google that allows for augmented reality applications in the real world. These tools include environmental understanding, which allows devices to detect horizontal and vertical surfaces and planes. It also includes motion tracking, which lets phones understand and track their positions relative to the world. Also ARCore’s Light Estimation API lets your digital objects appear realistically as if they’re actually part of the physical world.
[SteamVR](https://store.steampowered.com/steamvr) is the ultimate tool for experiencing VR content on the hardware of your choice. SteamVR supports the Valve Index, HTC Vive, Oculus Rift, Windows Mixed Reality headsets, and others.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/110880418-71718300-8293-11eb-986e-1b1f8cb49112.png">
<br />
SteamVR Home
</p>
[OpenVR](https://github.com/ValveSoftware/openvr) is an API and runtime that allows access to VR hardware(Steam Index, HTC Vive, and Oculus Rift) from multiple vendors without requiring that applications have specific knowledge of the hardware they are targeting.
[OpenVR Benchmark on Steam](https://store.steampowered.com/app/955610/OpenVR_Benchmark/) is the first benchmark tool for reproducibly testing your real VR performance, rendering inside of your VR headset.
[OpenHMD](http://www.openhmd.net/) is open source API and drivers that supports a wide range of HMD(head-mounted display) devices such as Oculus Rift, HTC Vive, Sony PSVR, and others.
[openXR](https://www.khronos.org/OpenXR/) is a free, open standard that provides high-performance access to Augmented Reality (AR) and Virtual Reality (VR) collectively known as XR—platforms and devices.
[Monado](https://monado.dev/) is the first OpenXR™ runtime for GNU/Linux. Monado aims to jump-start development of an open source XR ecosystem and provide the fundamental building blocks for device vendors to target the GNU/Linux platform.
[Libsurvive](https://github.com/cntools/libsurvive) is a set of tools and libraries that enable 6 dof tracking on lighthouse and vive based systems that is completely open source and can run on any device. It currently supports both SteamVR 1.0 and SteamVR 2.0 generation of devices and should support any tracked object commercially available.
[Simula](https://github.com/SimulaVR/Simula) is a VR window manager for Linux that runs on top of Godot. It takes less than 1 minute to install. Simula is officially compatible with SteamVR headsets equipped with Linux drivers (e.g. HTC Vive, HTC Vive Pro, & Valve Index). We have also added experimental support to OpenXR headsets that have Monado drivers (e.g. North Star, OSVR HDK, and PSVR). Some people have gotten the Oculus Rift S to run Simula via OpenHMD ([see here](https://github.com/OpenHMD/OpenHMD/issues/225#issuecomment-638454156)).
## Game Development Learning Resources
[Unreal Online Learning](https://www.unrealengine.com/en-US/onlinelearning-courses) is a free learning platform that offers hands-on video courses and guided learning paths.
[Unreal Engine Authorized Training Program](https://www.unrealengine.com/en-US/training-partners)
[Unreal Engine for education](https://www.unrealengine.com/en-US/education/)
[Unreal Engine Training & Simulation](https://www.unrealengine.com/en-US/industry/training-simulation)
[Unity Certifications](https://unity.com/products/unity-certifications)
[Getting Started with Vulkan](https://www.khronos.org/vulkan/)
[Getting Started with Apple Metal](https://developer.apple.com/metal/)
[Game Design Online Courses from Udemy](https://www.udemy.com/courses/Design/Game-Design/)
[Game Design Online Courses from Skillshare](https://www.skillshare.com/browse/game-design)
[Learn Game Design with Online Courses and Classes from edX](https://www.edx.org/learn/game-design)
[Game Design Courses from Coursera](https://www.coursera.org/courses?query=game%20design)
[Game Design and Development Specialization Course from Coursera](https://www.coursera.org/specializations/game-development)
# OpenGL Development
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/131386211-f507b5d4-a3c9-4c21-aadd-2aa5bde94d1e.png">
<br />
</p>
## OpenGL Learning Resources
[Open Graphics Library(OpenGL)™](https://www.opengl.org/) is an API used acrossed mulitple programming languages and platforms for hardware-accelerated rendering of 2D/3D vector graphics currently developed by the [Khronos Group](https://www.khronos.org/).
[OpenGL ES™](https://www.khronos.org/opengles/) is the mobile subset of OpenGL for Embedded Systems(ES). It's supported on all major mobile platforms, and is also the base for WebGL.
[WebGL™](https://www.khronos.org/webgl/) is a cross-platform, royalty-free web standard for a low-level 3D graphics API based on OpenGL ES, exposed to JavaScript via the HTML5 Canvas element.
[Khronos Group | GitHub](https://github.com/KhronosGroup/)
[Khronos Technology Courses and Training](https://www.khronos.org/developers/training/)
[Top OpenGL Courses Online | Coursera](https://www.coursera.org/courses?query=opengl&page=1)
[Top OpenGL Courses Online | Udemy](https://www.udemy.com/topic/opengl/)
[OpenGL Online Training Courses | LinkedIn Learning](https://www.linkedin.com/learning/topics/opengl)
[Getting Started with OpenGL](https://www.khronos.org/opengl/wiki/Getting_Started)
[OpenGL Reference Cards](https://www.khronos.org/developers/reference-cards/)
[Getting Started with OpenGL ES](https://www.khronos.org/opengles/)
[OpenGL ES Reference Cards](https://www.khronos.org/developers/reference-cards/)
[Getting Started with WebGL](https://www.khronos.org/webgl/)
[WebGL 2.0 Specification](https://www.khronos.org/registry/webgl/specs/latest/2.0/)
[WebGL Public Wiki](https://www.khronos.org/webgl/wiki)
[WebGL Reference Cards](https://www.khronos.org/developers/reference-cards/)
## OpenGL Tools, Libraries, and Frameworks
[BuGLe](https://www.opengl.org/sdk/tools/BuGLe/) is a debugger for Linux and other UNIX-like OSes. BuGLe combines a graphical OpenGL debugger with a selection of filters on the OpenGL command stream. The debugger allows viewing of state, textures, framebuffers and shaders, while the filters allow for logging, error checking, video capture and more.
[gDEBugger](https://www.opengl.org/sdk/tools/gDEBugger/) is a full-featured and free debugger and profiler representing the state-of-the-art in OpenGL and OpenGL ES debugging and profiling on Windows and Linux.
[KTX](http://www.khronos.org/opengles/sdk/tools/KTX/) is a lightweight file format for delivering textures to OpenGL family APIs.
[RenderDoc](https://renderdoc.org) is a stand-alone graphics debugger that allows quick and easy single-frame capture and detailed introspection of any application using Vulkan, D3D11, OpenGL & OpenGL ES or D3D12 across Windows, Linux, Android, Stadia, or Nintendo Switch™.
[NVIDIA® Nsight™ Visual Studio Edition](https://developer.nvidia.com/nsight-visual-studio-edition) is an application development environment for heterogeneous platforms which brings GPU computing into Microsoft Visual Studio. NVIDIA Nsight™ VSE allows you to build and debug integrated GPU kernels and native CPU code as well as inspect the state of the GPU and memory.
[Radeon™ GPU Profiler](https://gpuopen.com/rgp/) is a performance tool that can be used by developers to optimize DirectX®12, Vulkan® and OpenCL™ applications for AMD RDNA™ and GCN hardware.
[Radeon™ GPU Analyzer](https://gpuopen.com/rga/) is a compiler and code analysis tool for Vulkan®, DirectX®, OpenGL® and OpenCL™.
[AMD Radeon ProRender](https://www.amd.com/en/technologies/radeon-prorender) is a powerful physically-based rendering engine that enables creative professionals to produce stunningly photorealistic images on virtually any GPU, any CPU, and any OS in over a dozen leading digital content creation and CAD applications.
[NVIDIA Omniverse](https://developer.nvidia.com/nvidia-omniverse-platform) is a powerful, multi-GPU, real-time simulation and collaboration platform for 3D production pipelines based on Pixar's Universal Scene Description and NVIDIA RTX.
[MoltenGL](https://moltengl.com) is an implementation of the OpenGL ES 2.0 API that runs on Apple's [Metal](https://developer.apple.com/metal/) graphics framework.
[EGL](https://www.khronos.org/egl/) is an interface between Khronos rendering APIs such as OpenGL or OpenVG and the underlying native platform window system.
[Equalizer](https://www.opengl.org/sdk/libs/Equalizer/) is an open source programming interface and resource management system for scalable OpenGL applications. An Equalizer application can be deployed on any visualization system, from a singlepipe workstation to large scale graphics clusters.
[GLee](https://www.opengl.org/sdk/libs/GLee/) is a free cross-platform extension loading library that takes the burden off your application. GLee makes it easy to check for OpenGL extension and core version availability, automatically setting up the entry points with no effort on your part.
[GLEW](https://www.opengl.org/sdk/libs/GLEW/) is an open-source cross-platform extension loading library with thread-safe support for multiple rendering contexts and automatic code generation capability. GLEW provides easy-to-use and efficient methods for checking OpenGL extensions and core functionality.
[GLUS](https://www.opengl.org/sdk/libs/GLUS) is an open-source C library, which provides a hardware and operating system abstraction plus many functions usually needed for graphics programming using OpenGL, OpenGL ES or OpenVG.
[OpenGL Mathematics (GLM)](http://glm.g-truc.net/) is a C++ mathematics library for 3D software based on the OpenGL Shading Language (GLSL) specification.
[libktx](http://www.khronos.org/opengles/sdk/tools/KTX/index.php#libktx) is a library of functions(part of the [KTX tool set](http://www.khronos.org/opengles/sdk/tools/KTX)) for writing [KTX format files](http://www.khronos.org/opengles/sdk/tools/KTX/file_format_spec/) and instantiating GL textures from them.
[OpenSceneGraph](https://www.opengl.org/sdk/libs/OpenSceneGraph/) is a high-level 3D graphics toolkit exposing OpenGL's capabilities while providing many capabilities of its own. OpenSceneGraph boasts a large user community and has been employed for visual simulation, games, virtual reality, scientific visualization, and modeling.
[Mesa 3D Graphics Library](https://docs.mesa3d.org/index.html) is a project that began as an open-source implementation of the OpenGL specification. A system for rendering interactive 3D graphics. Mesa ties into several other open-source projects: the [Direct Rendering Infrastructure](https://dri.freedesktop.org/), [X.org](https://x.org/), and [Wayland](https://wayland.freedesktop.org/) to provide OpenGL support on Linux, FreeBSD, and other operating systems.
# Vulkan Development
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/129622224-8c4cca51-9200-4d70-9d16-2610d704713a.png">
<br />
</p>
## Vulkan Learning Resources
[Vulkan®](https://www.khronos.org/vulkan/) is a modern cross-platform graphics and compute API that provides high-efficiency, cross-platform access to modern GPUs used in a wide variety of devices from PCs and consoles to mobile phones and embedded platforms. Vulkan is currently in development by the Khronos consortium.
[Khronos Group GitHub](https://github.com/KhronosGroup)
[Vulkan Documentation](https://github.com/KhronosGroup/Vulkan-Docs)
[HLSL to SPIR-V Feature Mapping Manual](https://github.com/microsoft/DirectXShaderCompiler/blob/master/docs/SPIR-V.rst)
[Vulkan GLSL Ray Tracing Emulator Tutorial](https://www.gsn-lib.org/docs/nodes/raytracing.php)
[Getting Started with Vulkan](https://vulkan-tutorial.com/)
[Vulkan Samples](https://github.com/KhronosGroup/Vulkan-Samples)
[Khronos Community Forums](https://community.khronos.org/)
## Vulkan Tools, Libraries, and Frameworks
[Vulkan SDK](https://vulkan.lunarg.com) is a set of tools that enables Vulkan developers to develop Vulkan applications.
[SPIR-V](https://www.khronos.org/spir/) is a set of tools that enables high-level language front-ends to emit programs in a standardized intermediate form to be ingested by Vulkan, OpenGL or OpenCL drivers. It eliminates the need for high-level language front-end compilers in device drivers, significantly reducing driver complexity, enables a broad range of language and framework front-ends to run on diverse hardware architectures and encourages a vibrant ecosystem of open source analysis, porting, debug and optimization tools.
[SPIRV-Reflect](https://github.com/KhronosGroup/SPIRV-Reflect) is a lightweight library that provides a C/C++ reflection API for SPIR-V shader bytecode in Vulkan applications.
[Vulkan® Tools](https://github.com/KhronosGroup/Vulkan-Tools) is a project that provides Khronos official Vulkan Tools and Utilities for Windows, Linux, Android, and macOS.
[Vulkan-Hpp](https://github.com/KhronosGroup/Vulkan-Hpp) is a API that provides a header only C++ bindings for the Vulkan C API to improve the developers Vulkan experience without introducing CPU runtime cost. It adds features like type safety for enums and bitfields, STL container support, exceptions and simple enumerations.
[Vulkan® Memory Allocator (VMA)](https://gpuopen.com/vulkan-memory-allocator/) is a library that provides a simple and easy to integrate API to help you allocate memory for Vulkan® buffer and image storage.
[AMD Open Source Driver for Vulkan®](https://gpuopen.com/amd-open-source-driver-for-vulkan/) is an open-source Vulkan driver for AMD Radeon™ graphics adapters on Linux®.
[NVIDIA® Nsight™ Visual Studio Edition](https://developer.nvidia.com/nsight-visual-studio-edition) is an application development environment for heterogeneous platforms which brings GPU computing into Microsoft Visual Studio. NVIDIA Nsight™ VSE allows you to build and debug integrated GPU kernels and native CPU code as well as inspect the state of the GPU and memory.
[Radeon™ GPU Profiler](https://gpuopen.com/rgp/) is a performance tool that can be used by developers to optimize DirectX®12, Vulkan® and OpenCL™ applications for AMD RDNA™ and GCN hardware.
[Radeon™ GPU Analyzer](https://gpuopen.com/rga/) is a compiler and code analysis tool for Vulkan®, DirectX®, OpenGL® and OpenCL™.
[Radeon™ Memory Visualizer (RMV)](https://gpuopen.com/rmv/) is a tool provided by AMD for use by game engine developers. It allows engineers to examine, diagnose, and understand the GPU memory management within their projects.
[DXVK](https://github.com/doitsujin/dxvk) is a Vulkan-based translation layer for Direct3D 9/10/11 which allows running 3D applications on Linux using Wine.
[MoltenVK](https://moltengl.com/moltenvk) is an implementation of Vulkan running on iOS and macOS using Apple's [Metal](https://developer.apple.com/metal/) graphics framework.
[RenderDoc](https://renderdoc.org) is a stand-alone graphics debugger that allows quick and easy single-frame capture and detailed introspection of any application using Vulkan, D3D11, OpenGL & OpenGL ES or D3D12 across Windows, Linux, Android, Stadia, or Nintendo Switch™.
[PerfDoc](https://github.com/ARM-software/perfdoc) is a cross-platform Vulkan layer which checks Vulkan applications for [best practices on Arm Mali](https://developer.arm.com/graphics/developer-guides/mali-gpu-best-practices) devices.
[GLFW](https://www.glfw.org/) is an Open Source, multi-platform library for OpenGL, OpenGL ES and Vulkan application development. It provides a simple, platform-independent API for creating windows, contexts and surfaces, reading input, handling events, etc. GLFW natively supports Windows, macOS and Linux and other Unix-like systems. On Linux both X11 and Wayland are supported.
[VulkanSharp](https://github.com/mono/VulkanSharp) is a project provides a .NET binding for the Vulkan API.
[Vortice.Vulkan](https://github.com/amerkoleci/Vortice.Vulkan) is a .NET Standard 2.0 and .NET5 low-level bindings for Vulkan API.
[VKD3D-Proton](https://github.com/HansKristian-Work/vkd3d-proton) is a fork of VKD3D, which aims to implement the full Direct3D 12 API on top of Vulkan.
[ImGui](https://github.com/ocornut/imgui) is a bloat-free graphical user interface library for C++. It outputs optimized vertex buffers that you can render anytime in your 3D-pipeline enabled application. It is fast, portable, renderer agnostic and self-contained (no external dependencies).
[Ash](https://github.com/MaikKlein/ash) is a very lightweight wrapper around Vulkan.
[gfx-rs](https://github.com/gfx-rs/gfx) is a low-level, cross-platform graphics and compute abstraction library in Rust.
[Vulkan.jl](https://github.com/JuliaGPU/Vulkan.jl) is a lightweight wrapper around the Vulkan graphics and compute library. It exposes abstractions over the underlying C interface, primarily geared towards developers looking for a more natural way to work with Vulkan with minimal overhead.
# DirectX Development
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/129622295-1bd0e123-57bb-4460-baab-8dbc8818052a.png">
<br />
</p>
## DirectX Learning Resources
[Microsoft DirectX®](https://support.microsoft.com/en-us/topic/how-to-install-the-latest-version-of-directx-d1f5ffa5-dae2-246c-91b1-ee1e973ed8c2) is a low-level API that handles tasks related to multimedia for game programming and video on Microsoft platforms(Windows & Xbox).
[Getting Started with DirectX 12 Ultimate](https://devblogs.microsoft.com/directx/directx-12-ultimate-getting-started-guide/)
[Getting Started with the DirectX 12 Agility SDK](https://devblogs.microsoft.com/directx/gettingstarted-dx12agility/)
[DirectX 12 and Graphics Education | YouTube](https://www.youtube.com/channel/UCiaX2B8XiXR70jaN7NK-FpA)
[DirectX— Feature Level 12_2](https://devblogs.microsoft.com/directx/new-in-directx-feature-level-12_2/)
[DirectX 12 Technology | NVIDIA](https://www.nvidia.com/en-us/geforce/technologies/dx12/)
[AMD DirectX® 12 (DX12) Technology | AMD](https://www.amd.com/en/technologies/directx12)
[Top Microsoft DirectX Courses Online | Udemy](https://www.udemy.com/topic/microsoft-directx/)
[DirectX - Learn Microsoft DirectX from Scratch Course | Udemy](https://www.udemy.com/course/directx-learn-microsoft-directx-from-scratch/)
[DirectX 11 Programming Course | Udemy](https://www.udemy.com/course/directx11/)
## DirectX Tools, Libraries, and Frameworks
[Visual Studio](https://visualstudio.microsoft.com/) is an integrated development environment (IDE) from Microsoft; which is a feature-rich application that can be used for many aspects of software development. Visual Studio makes it easy to edit, debug, build, and publish your app. By using Microsoft software development platforms such as Windows API, Windows Forms, Windows Presentation Foundation, and Windows Store.
[Visual Studio Code](https://code.visualstudio.com/) is a code editor redefined and optimized for building and debugging modern web and cloud applications.
[DirectX-Graphics-Samples](https://github.com/Microsoft/DirectX-Graphics-Samples) is a project that contains the DirectX 12 Graphics samples that demonstrate how to build graphics intensive applications for Windows 10.
[PIX on Windows](https://devblogs.microsoft.com/pix/documentation/) is a performance tuning and debugging tool for DirectX 12 games on Windows.
[DirectStorage API](https://devblogs.microsoft.com/directx/directstorage-is-coming-to-pc/) is an API in the DirectX family originally designed for the [Velocity Architecture](https://news.xbox.com/en-us/2020/07/14/a-closer-look-at-xbox-velocity-architecture/) to Windows. The DirectX API is architected in a way that takes all this into account and maximizes performance throughout the entire pipeline from NVMe drive all the way to the GPU. It does this in several ways: by reducing per-request NVMe overhead, enabling batched many-at-a-time parallel IO requests which can be efficiently fed to the GPU, and giving games finer grain control over when they get notified of IO request completion instead of having to react to every tiny IO completion. The DirectStorage API will be available on [Windows 11](https://www.microsoft.com/en-us/windows/windows-11) PCs with NVMe SSDs, but will also be support in [Windows 10](https://www.microsoft.com/software-download/windows10) version 1909 and newer.
[NVIDIA® Nsight™ Visual Studio Edition](https://developer.nvidia.com/nsight-visual-studio-edition) is an application development environment for heterogeneous platforms which brings GPU computing into Microsoft Visual Studio. NVIDIA Nsight™ VSE allows you to build and debug integrated GPU kernels and native CPU code as well as inspect the state of the GPU and memory.
[NVRHI (NVIDIA Rendering Hardware Interface)](https://github.com/NVIDIAGameWorks/nvrhi) is a library that implements a common abstraction layer over multiple graphics APIs (GAPIs): Direct3D 11, Direct3D 12, and Vulkan 1.2. It works on Windows (x64 only) and Linux (x64 and ARM64).
[RTXMU - RTX Memory Utility SDK](https://github.com/NVIDIAGameWorks/RTXMU) is an SDK tool that batchs up all of the acceleration structure build inputs and pass them to RTXMU which in turn will perform all the suballocation memory requests and build details including compaction. Then post build info is abstracted away by the SDK in order to do compaction under the hood. RTXMU returns acceleration structure handle ids that are used to reference the underlying memory buffers. These handle ids are passed into RTXMU to create compaction copy workloads, deallocate unused build resources or remove all memory associated with an acceleration structure.
[Radeon™ GPU Profiler](https://gpuopen.com/rgp/) is a performance tool that can be used by developers to optimize DirectX®12, Vulkan® and OpenCL™ applications for AMD RDNA™ and GCN hardware.
[Radeon™ GPU Analyzer](https://gpuopen.com/rga/) is a compiler and code analysis tool for Vulkan®, DirectX®, OpenGL® and OpenCL™.
[Radeon™ Memory Visualizer (RMV)](https://gpuopen.com/rmv/) is a tool provided by AMD for use by game engine developers. It allows engineers to examine, diagnose, and understand the GPU memory management within their projects.
[FNA](https://fna-xna.github.io/) is an XNA4 reimplementation that focuses solely on developing a fully accurate XNA4 runtime for the desktop.
[FAudio](https://fna-xna.github.io/) is an XAudio reimplementation that focuses solely on developing fully accurate DirectX Audio runtime libraries for the FNA project, including [XAudio2](https://docs.microsoft.com/en-us/windows/win32/xaudio2/xaudio2-introduction), [X3DAudio](https://docs.microsoft.com/en-us/windows/win32/xaudio2/x3daudio-overview), [XAPO](https://docs.microsoft.com/en-us/windows/win32/xaudio2/xapo-overview), and [XACT3](https://en.wikipedia.org/wiki/Cross-platform_Audio_Creation_Tool).
[Simple DirectMedia Layer](https://www.libsdl.org/) is a cross-platform development library designed to provide low level access to audio, keyboard, mouse, joystick, and graphics hardware via OpenGL and Direct3D. It is used by video playback software, emulators, and popular games including Valve's award winning catalog.
[DXVK](https://github.com/doitsujin/dxvk) is a Vulkan-based translation layer for Direct3D 9/10/11 which allows running 3D applications on Linux using Wine.
[VKD3D-Proton](https://github.com/HansKristian-Work/vkd3d-proton) is a fork of VKD3D, which aims to implement the full Direct3D 12 API on top of Vulkan.
[RenderDoc](https://renderdoc.org) is a stand-alone graphics debugger that allows quick and easy single-frame capture and detailed introspection of any application using Vulkan, D3D11, OpenGL & OpenGL ES or D3D12 across Windows, Linux, Android, Stadia, or Nintendo Switch™.
# Professional Audio/Video Development
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/100922475-caf73400-3492-11eb-88ac-d0976f3057d3.png">
<br />
</p>
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/119560582-f62f5180-bd58-11eb-842b-fcf4330f3e2f.png">
<br />
</p>
## Audio/Video Learning Resources
[10 Video Production Tools That Make the Difference Between a Good Video and a Bad One by Dann Albright ](https://www.uscreen.tv/blog/video-production-equipment/)
[Online Audio Editing Classes from Skillshare](https://www.skillshare.com/browse/audio-editing)
[Online Video Editing Classes Skillshare](https://www.skillshare.com/search?query=Video-Editing)
[Video Editing Courses from Udemy](https://www.udemy.com/topic/video-editing/)
[Audio Editing Courses from udemy](https://www.udemy.com/topic/audio-editing/)
[Video Editing Courses from Coursera](https://www.coursera.org/courses?query=video%20editing)
[The Basics of Audio Editing from Coursera](https://www.coursera.org/lecture/vocal-production/basic-audio-editing-6JLBJ)
[Audacity Podcast Production Course: Audio Editing for Podcasters](https://www.thepodcasthost.com/academy/course-library/audacity-course/)
[Video Editing Online Training Courses from LinkedIn Learning](https://www.linkedin.com/learning/topics/video-editing)
[Audio Editing Online Training Courses from LinkedIn Learning](https://www.linkedin.com/learning/topics/audio-editing)
## Audio/Video Tools and Equipment
[H.264(AVC)](https://en.wikipedia.org/wiki/H.264/MPEG-4_AVC) is a video compression standard based on block-oriented and motion-compensated integer-DCT coding that defines multiple profiles (tools) and levels (max bitrates and resolutions) with support up to 8K.
[H.265(HEVC)](https://en.wikipedia.org/wiki/High_Efficiency_Video_Coding) is a video compression standard that is the successor to H.264(AVC). It offers a 25% to 50% better data compression at the same level of video quality, or improved video quality at the same bit-rate.
[FFmpeg](https://ffmpeg.org) is a leading multimedia framework that can decode, encode, transcode, mux, demux, stream, filter and play pretty much anything that humans and machines have created. It supports the most obscure ancient formats up to the cutting edge ones on multiple platforms such as Windows, macOS, and Linux.
[HandBrake](https://handbrake.fr/) is a tool for transcoding video from almost any format with a selection of widely supported codecs. It is supported on Window, macOS, and Linux.
[HTTP Live Streaming (HLS)](https://developer.apple.com/streaming/) is a communications protocol developed by Apple that sends live and on‐demand audio and video to iPhone, iPad, Mac, Apple Watch, Apple TV, and PC.
[Dynamic Adaptive Streaming over HTTP (DASH)](https://developer.mozilla.org/en-US/docs/Web/HTML/DASH_Adaptive_Streaming_for_HTML_5_Video) is an adaptive streaming protocol that allows for a video stream to switch between bit rates on the basis of network performance, in order to keep a video playing.
[OpenMAX™](https://www.khronos.org/openmax/) is a cross-platform API that provides comprehensive streaming media codec and application portability by enabling accelerated multimedia components to be developed, integrated and programmed across multiple operating systems and silicon platforms.
[GStreamer](https://gstreamer.freedesktop.org/) is a library for constructing graphs of media-handling components. The applications it supports range from simple Ogg/Vorbis playback, audio/video streaming to complex audio (mixing) and video (non-linear editing) processing. Applications can take advantage of advances in codec and filter technology transparently.
[Media Source Extensions (MSE)](https://www.w3.org/TR/media-source/) is a [W3C specification](https://github.com/w3c/media-source) that allows JavaScript to send byte streams to media codecs within Web browsers that support HTML5 video and audio. Also, this allows the implementation of client-side prefetching and buffering code for streaming media entirely in JavaScript.
[WebRTC](https://webrtc.org/) is an open-source project that adds real-time communication capabilities to your application that works on top of an open standard. It supports video, voice, and generic data to be sent between peers, allowing developers to build powerful voice- and video-communication solutions.
[Apple ProRes](https://support.apple.com/en-us/HT200321) is a codec technology developed by Apple for high-quality & high-performance editing in Final Cut Pro that supports up to 8K.
[Premiere Pro](https://www.adobe.com/products/premiere.html) is the industry-leading video editing software for film, TV, and the web. Creative tools, integration with other apps and services, and the power of Adobe Sensei help you craft footage into polished films and videos. With [Premiere Rush](https://www.adobe.com/products/premiere-rush.html) you can create and edit new projects from any device.
[Final Cut Pro](https://www.apple.com/final-cut-pro/) is Apple's professional grade video editing software.
[Kdenlive](https://kdenlive.org/en/) is an open source video editing tool that supports unlimited multimedia files. It's based on the MLT Framework, KDE and Qt. People who are looking for a very versatile video editing tool that comes packed with features. The latest 20.08 release is out with nifty features like Interface Layouts, Multiple Audio Stream support, Cached data management and Zoombars in the Clip Monitor and Effects Panel but one may argue that the highlights of this release are stability and interface improvements.
[OpenShot](https://www.openshot.org/) is an open-source video editing tool that's designed for users new in the editing environment. It has simple features such as a simple drag-and-drop function, it provides an easy-to-use and quick-to-learn user interface. The powerful video editor offers tons of efficient ways to cut and trim down your videos. You can freely utilize the unlimited tracks, video effects engine, title editor, 3D animations, slow motion, and time effects. It supports commonly used video codecs that are supported by FFmpeg like WebM (VP9), AVCHD (libx264), HEVC (libx265) and audio codecs like mp3 (libmp3lame) and aac (libfaac). The program can render MPEG4, ogv, Blu-ray and DVD video, and Full HD videos for uploading to the internet video websites like YouTube.
[DaVinci Resolve](https://www.blackmagicdesign.com/products/davinciresolve/) is the world’s only solution that combines professional 8K editing, color correction, visual effects and audio post production all in one software tool! You can instantly move between editing, color, effects, and audio with a single click. DaVinci Resolve Studio is also the only solution designed for multi user collaboration so editors, assistants, colorists, VFX artists and sound designers can all work live on the same project at the same time.
[Blender](https://www.blender.org/features/video-editing/) comes with a built-in video sequence editor allows you to perform basic actions like video cuts and splicing, as well as more complex tasks like video masking or color grading. The Video Editor includes: Live preview, luma waveform, chroma vectorscope and histogram displays. Audio mixing, syncing, scrubbing and waveform visualization.
[Lightworks](https://www.lwks.com/) is a non-linear video editing appluication for editing and mastering digital video used by the film industry. Its professional edition has been used for box office hits, such as Shutter Island, Pulp Fiction, and Mission Impossible. Intimidating user interface. Like professional video editors, such as Adobe Premiere Pro, Lightworks is rather complicated to use for new users.
[Shotcut](https://www.shotcut.org/) is an open source multi-platform video editor. You can perform various actions such as video editing (including 4K video quality), add effects, create new movies, import most image files formats, export to almost any file format and much more.
[Olive](https://www.olivevideoeditor.org) is a free non-linear video editor aiming to provide a fully-featured alternative to high-end professional video editing software.
[OBS (Open Broadcaster Software)](https://obsproject.com/) is free and open source software for video recording and live streaming. Stream to Twitch, YouTube and many other providers or record your own videos with high quality H264 / AAC encoding.
[REAPER](https://www.reaper.fm/) is a complete digital audio production application for computers, offering a full multitrack audio and MIDI recording, editing, processing, mixing and mastering toolset.REAPER supports a vast range of hardware, digital formats and plugins, and can be comprehensively extended, scripted and modified.
[Logic Pro](https://www.apple.com/logic-pro/) is a digital audio workstation (DAW) and MIDI sequencer software application for macOS.
[Ableton Live](https://www.ableton.com/live) is a digital audio workstation application. It's designed to be an instrument for live performances as well as a tool for composing, recording, arranging, mixing, and mastering.
[Bitwig Studio](https://www.bitwig.com) is a digital audio workstation that has linear and non-linear workflows for sound design, recording, live performance, and more. Along with 90+ instruments, effects, and other creative tools. It is supported Windows, macOS, and Linux.
[SonoBus](https://sonobus.net) is an easy to use application for streaming high-quality, low-latency peer-to-peer audio between devices over the internet or a local network.
[Avid Pro Tools](https://www.avid.com/pro-tools) is a digital audio workstation for mixing Dolby Atmos or other 3D audio for film or TV, working in a large mixing stage or a small editing suite.
[Adobe Audition](https://www.adobe.com/products/audition.html) is a digital audio workstation with tools for color, audio, and graphics, Premiere Pro works seamlessly with other apps and services, including After Effects, Adobe Audition, and Adobe Stock.
[JACK Audio Connection Kit AKA JACK](https://jackaudio.org/) is a professional sound server daemon that provides real-time, low-latency connections for both audio and MIDI data between applications that implement its API. JACK can be configured to send audio data over a network to a main machine, which then outputs the audio to a physical device. This can be useful to mix audio from a number of linked computers without requiring additional cables or hardware mixers, and keeping the audio path digital for as long as possible.
[JACK2](https://github.com/jackaudio/jack2) is a C++ version of the JACK low-latency audio server for multi-processor machines. It is a new implementation of the JACK server core features that aims at removing some limitations of the JACK1 design. The activation system has been changed for a data flow model and lock-free programming techniques for graph access have been used to have a more dynamic and robust system.
[Image-Line FL Studio](https://www.image-line.com/fl-studio/) is a digital audio workstation for mixing audio and music sequencing developed by Image-Line.
[Propellerhead Reason](https://www.reasonstudios.com/) is a digital audio workstation with tools you need for creative music production, recording, sequencing and mixing.
[Cockos Reaper](https://www.cockos.com/reaper/) is a full, flexible feature set digital audio workstation with tools used for commercial/home studios, broadcast, location recording, education, science/research, sound design, game development, and more.
[PreSonus Studio One](https://www.presonus.com/products/studio-one) is a digital audio workstation application, used to create, record, mix and master music and other audio.
[Steinberg Cubase](https://new.steinberg.net/cubase/) is a digital audio workstation developed by Steinberg for music/MIDI recording and arranging/editing of audio.
[Digital Performer](https://motu.com/products/software/dp/) is a digital audio workstation and music sequencer software package. It's can recording, editing, arranging, mixing, mastering, MIDI, and more. Digital Performer will take your music to its highest level, thanks to sophisticated features like Stretch Audio (powered by Zynaptiq’s ZTX PRO technology), live triggering and looping.
[Sonic Visualiser](https://www.sonicvisualiser.org) is a free, open-source application for Windows, Linux, and Mac, designed to be the first program you reach for when want to study a music recording closely.
[PipeWire](https://pipewire.org) is a server and user space API to deal with multimedia pipelines.It provides a low-latency, graph based processing engine on top of audio and video devices that can be used to support the use cases currently handled by both pulseaudio and JACK. PipeWire was designed with a powerful security model that makes interacting with audio and video devices from containerized applications easy. Nodes in the graph can be implemented as separate processes, communicating with sockets and exchanging multimedia content using fd passing.
[LMMS](https://lmms.io/) is an open source digital audio workstation application program. When LMMS is pairedr with appropriate computer hardware, it allows music to be produced by arranging samples, synthesizing sounds, playing on a MIDI keyboard, and combining the features of trackers and sequencers. Developed by Paul Giblock and Tobias Junghans, this program stands for "Linux MultiMedia Studio" and supports handy plugins that enables it to work on different operating systems.
[Ardour](http://ardour.org/) is an open source, collaborative effort of a worldwide team including musicians, programmers, and professional recording engineers. Development is transparent — anyone can watch our work as it happens. Like a good piece of vintage hardware, you can open the box and look inside.
[Audacity](https://www.audacityteam.org/) is an easy-to-use, multi-track audio editor and recorder for Windows, Mac OS X, GNU/Linux and other operating systems. Developed by a group of volunteers as open source and offered free of charge. Amazing support community.
# 3D Graphics and Design
[Back to the Top](https://github.com/mikeroyal/GPU-Guide#table-of-contents)
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/97116104-27a74800-16b8-11eb-9556-bdb90ba45ce7.png">
<br />
</p>
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/126396094-16e1b14c-e1a2-4086-803d-5a8d99edeade.png">
<br />
</p>
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/118338215-6ec91f00-b4ca-11eb-9c9e-f5cf377ca3c4.png">
<br />
</p>
**Blender's Cycles Render Engine. Source: [Blender](https://www.blender.org)**
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/118338217-6ffa4c00-b4ca-11eb-92d0-9aa30230495d.png">
<br />
</p>
**Blender VFX(Visual Effects). Source: [Blender](https://www.blender.org)**
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/118338223-72f53c80-b4ca-11eb-8100-2e2abd98c910.png">
<br />
</p>
**Designing a simple UI/UX for an application in Adobe XD. Source: [Adobe](https://www.adobe.com/products/xd.html)**
## 3D Graphics and Design Learning Resources
[Introduction to Pixar's Universal Scene Description (USD)](https://graphics.pixar.com/usd/docs/index.html)
[Intro to using Universal Scene Description with NVIDIA](https://developer.nvidia.com/usd)
[First steps with Universal Scene Description in Blender](https://code.blender.org/2019/07/first-steps-with-universal-scene-description/)
[AMD Radeon ProRender Developer Suite](https://gpuopen.com/radeon-prorender-suite/)
[Blender Foundation](https://www.blender.org/foundation/)
[Blender Foundation Certification Training](https://www.blender.org/certification/)
[Blender Cloud Courses](https://cloud.blender.org/courses)
[Blender Institute](https://www.blender.org/institute/)
[Blender Education](https://www.blender.org/get-involved/)
[Blender Network](https://www.blendernetwork.org/)
[Blender Courses from Udemy](https://www.udemy.com/topic/blender/)
[AutoDesk Learning, Training & Certification](https://www.autodesk.com/training)
[AutoDesk Design Academy](https://academy.autodesk.com)
[AutoDesk Courses and Specializations from Coursera](https://www.coursera.org/autodesk)
[Cinema 4D Quick Tips](https://www.cineversity.com/vidplaylist/cinema_4d_quick_tips)
[Getting Started with Cinema 4D](https://www.cineversity.com/vidplaylist/getting_started_with_cinema_4d_r20)
[Graphic Design Masterclass(Photoshop, Illustrator, InDesign) from Udemy](https://www.udemy.com/course/graphic-design-masterclass-everything-you-need-to-know/)
[Vectr: Beginner's Guide To Graphic Design from udemy](https://www.udemy.com/course/vectr-beginners-guide-to-graphic-design/)
[Canva MasterClass: Design For EveryDay Use from Udemy](https://www.udemy.com/course/canva-masterclass-design-for-everyday-use/)
## 3D Graphics and Design Tools
[Adobe Creative Cloud](https://www.adobe.com/creativecloud.html) is a collection of 20+ desktop and mobile apps(Lightroom, Photoshop, Illustrator, InDesign, Rush, Etc.) and services for photography, design, video, web, UX, and more.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/126396097-921982e9-5b6b-4f1e-90f3-9b7033eeb9ca.png">
<br />
</p>
[Autodesk®](https://www.autodesk.com/) is a collection of professional software products and services used in 3D design, architecture, engineering, manufacturing, and high production entertainment companies.
[Maya®](https://www.autodesk.com/products/maya/overview) is a 3D computer animation, modeling, simulation, and rendering software that can create realistic effects from explosions to cloth simulation.
[Maya LT™](https://www.autodesk.com/products/maya-lt/overview) is a game design software for indie game makers that can create and animate realistic-looking characters, props, and environments using the sophisticated 3D modeling and animation tools.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/122687226-2a682780-d1ca-11eb-9408-4b32ddc7d493.png">
<br />
</p>
**Autodesk® Maya. Source:[Autodesk](https://www.autodesk.com/products/maya/overview)**
[3DS Max®](https://www.autodesk.com/products/3ds-max/overview) is a 3D modeling and rendering software for design visualization, games, and animation.
<p align="center">
<img src="https://user-images.githubusercontent.com/45159366/122687233-3358f900-d1ca-11eb-8e9c-0cb68b16db0e.png">
<br />
</p>
**Autodesk® 3DS Max. Source:[Autodesk](https://www.autodesk.com/products/3ds-max/overview)**
[Arnold](https://www.autodesk.com/products/arnold/overview) is an advanced Monte Carlo ray tracing(Global illumination) renderer that helps you work more efficiently.
[ReCap™](https://www.autodesk.com/products/recap/overview) is a Pro 3D scanning software to transform the physical world into a digital asset. With reality capture data you can better understand and verify existing and as-built conditions to gain insights and make better decisions.
[Flame®](https://www.autodesk.com/products/flame/overview) is a 3D VFX and finishing software provides powerful tools for 3D compositing, visual effects, and editorial finishing. An integrated, creative environment means faster compositing, advanced graphics, color correction, and more.
[Mudbox®](https://www.autodesk.com/products/mudbox/overview) is a 3D digital painting and sculpting software that let's you sculpt and paint highly detailed 3D geometry and textures.
<p align="center">
<img src="https://user-images.githubuse
gitextract_e1y_1wpt/ ├── Getting Started with GPU Development.sh └── README.md
Condensed preview — 2 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (287K chars).
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About this extraction
This page contains the full source code of the mikeroyal/GPU-Guide GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 2 files (276.4 KB), approximately 64.7k tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.